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1

Cytoplasm and nuclei as a basis for Bethesda cell cluster classification in cervical smears

Pater Antonina, Roszkowiak Lukasz, Siemion Krzysztof, Zak Jakub, Deptuch Karol, Korzynska Anna

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 3

414--425

EN

Population screening in the form of cervical smears was introduced to reduce cervical cancer morbidity. However, the manual evaluation of cervical smears is a labour-intensive and meticulous task. This challenge has led to the development of various computer-aided cell identification methods aimed at improving the quality of evaluations and reducing the time required for slide analysis. These supportive tools for pathologists should adhere to the Bethesda classification system for cell types, facilitating integration with established clinical practices. Despite advances, the automatic classification of clustered cells in cervical smears remains a significant challenge for both automated and semiautomated methods that utilize classical image processing and deep learning techniques. This study introduces a novel method for fragmenting clusters to improve the classification of clustered cells in digital images of Papanicolaou smears. The proposed method integrates explainable AI and marker-guided watershed segmentation ensuring both interpretability and versatility of an overall pipeline for diagnostician support in smear evaluation. Validation of the method was performed on a recently developed Papanicolaou cytology Bialystok dataset, as well as the publicly available CRIC dataset, demonstrating the method’s generalizability across different datasets. The cell classification pipeline, enhanced by the proposed declustering method, improved the overall harmonic mean of recall and precision (F1 score) by 13.27 percentage points compared with the results obtained without this additional processing. The improvement in classifying the most critical cell type according to the Bethesda system (HSIL cell class) was even more significant, with an increase of 35.72 percentage points compared with classifying the entire cluster.

2

Advancing eye disease detection: A comprehensive study on computer-aided diagnosis with vision transformers and SHAP explainability techniques

Balaha Hossam Magdy, Hassan Asmaa El-Sayed, Ahmed Rawan Ayman, Balaha Magdy Hassan

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 1

23--33

EN

Eye diseases such as age-related macular degeneration (AMD) and diabetic retinopathy are common worldwide and affect millions of people. These conditions can cause severe vision problems and even lead to blindness if not treated promptly. Therefore, accurate and timely diagnosis is crucial to manage these diseases effectively and prevent irreversible vision loss. This study introduces a computer-aided diagnosis (CAD) framework for automatically detecting various eye diseases via advanced methodologies and datasets. The main focus is on classifying fundus images, which is essential for precise diagnosis and prognosis. By incorporating cutting-edge techniques such as Vision Transformers (ViTs), this study aims to improve the performance and interpretability of traditional Convolutional Neural Networks (CNNs). ViTs can capture complex patterns and long-range dependencies in fundus images, helping distinguish between different eye diseases and healthy conditions. Furthermore, the study integrates SHapley additive exPlanations (SHAP) explainability techniques to provide insights into the model’s decision-making process, enhancing trust and understanding of its predictions. The results demonstrate significant performance enhancements compared with the baseline models, with an overall accuracy of 95%. This method outperforms previous state-of-the-art methods by a considerable margin. Additionally, metrics such as precision, recall, intersection over union (IoU), and the Matthews correlation coefficient (MCC) show superior performance across various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. These findings underscore the effectiveness and reliability of the proposed approach in automated eye disease detection, indicating its potential for clinical integration and widespread adoption in healthcare settings.

3

BA-Net: Brightness prior guided attention network for colonic polyp segmentation

Xia Haiying, Qin Yilin, Tan Yumei, Song Shuixiang

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 3

603--615

EN

Automatic polyp segmentation at colonoscopy plays an important role in the early diagnosis and surgery of colorectal cancer. However, the diversity of polyps in different images greatly increases the difficulty of accurately segmenting polyps. Manual segmentation of polyps in colonoscopic images is time-consuming and the rate of polyps missed remains high. In this paper, we propose a brightness prior guided attention network (BA-Net) for automatic polyp segmentation. Specifically, we first aggregate the high-level features of the last three layers of the encoder with an enhanced receptive field (ERF) module, which further fed to the decoder to obtain the initial prediction maps. Then, we introduce a brightness prior fusion (BF) module that fuses the brightness prior information into the multi-scale side-out high-level semantic features. The BF module aims to induce the network to localize salient regions, which may be potential polyps, to obtain better segmentation results. Finally, we propose a global reverse attention (GRA) module to combine the output of the BF module and the initial prediction map for obtaining long-range dependence and reverse refinement prediction results. With iterative refinement from higher-level semantics to lower-level semantics, our BA-Net can achieve more refined and accurate segmentation. Extensive experiments show that our BA-Net outperforms the stateof-the-art methods on six common polyp datasets.

4

Segmentation of the melanoma lesion and its border

Surówka Grzegorz, Ogorzałek Maciej

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 4

683--699

EN

Segmentation of the border of the human pigmented lesions has a direct impact on the diagnosis of malignant melanoma. In this work, we examine performance of (i) morphological segmentation of a pigmented lesion by region growing with the adaptive threshold and density-based DBSCAN clustering algorithm, and (ii) morphological segmentation of the pigmented lesion border by region growing of the lesion and the background skin. Research tasks (i) and (ii) are evaluated by a human expert and tested on two data sets, A and B, of different origins, resolution, and image quality. The preprocessing step consists of removing the black frame around the lesion and reducing noise and artifacts. The halo is removed by cutting out the dark circular region and filling it with an average skin color. Noise is reduced by a family of Gaussian filters 3×3−7×7 to improve the contrast and smooth out possible distortions. Some other filters are also tested. Artifacts like dark thick hair or ruler/ink markers are removed from the images by using the DullRazor closing images for all RGB colors for a hair brightness threshold below a value of 25 or, alternatively, by the BTH transform. For the segmentation, JFIF luminance representation is used. In the analysis (i), out of each dermoscopy image, a lesion segmentation mask is produced. For the region growing we get a sensitivity of 0.92/0.85, a precision of 0.98/0.91, and a border error of 0.08/0.15 for data sets A/B, respectively. For the density-based DBSCAN algorithm, we get a sensitivity of 0.91/0.89, a precision of 0.95/0.93, and a border error of 0.09/0.12 for data sets A/B, respectively. In the analysis (ii), out of each dermoscopy image, a series of lesion, background, and border segmentation images are derived. We get a sensitivity of about 0.89, a specificity of 0.94 and an accuracy of 0.91 for data set A, and a sensitivity of about 0.85, specificity of 0.91 and an accuracy of 0.89 for data set B. Our analyses show that the improved methods of region growing and density-based clustering performed after proper preprocessing may be good tools for the computer-aided melanoma diagnosis.

5

Microaneurysm detection in fundus images based on a novel end-to-end convolutional neural network

Liao Yinhan, Xia Haiying, Song Shuxiang, Li Haisheng

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

589--604

EN

Microaneurysms are the earliest symptom of diabetic retinopathy and play an important role in the screening of diabetic retinopathy. However, because of the complex background, automatic detection microaneurysm in fundus images is a challenging task. Firstly, motivated by the characteristics of microaneurysm, a novel deep convolutional encoder-decoder network for microaneurysm detection is designed to locate the MAs by the differences between the skip connection in the network. Then, a weighted dice loss, termed the smooth dice loss, is presented to put more focus on misclassified microaneurysms. Finally, an activation function with a long tail is used to produce an accurate probability map for MA detection. Plenty of experiments, conducted on the Retinopathy Online Challenge data-set and the e-ophtha-MA dataset, demonstrate that the proposed model achieves the comparable performance to the existing state-of-the-art methods on microaneurysm detection with only one-hundredth the running time compared with its counterparts. The proposed method is simple and effective, guarantees the performance while shortening the test time. It indicates the potential application in the auxiliary diagnosis of diabetic retinopathy screening.

6

Impact of noise on the performance of automatic systems for vocal fold lesions detection

Madruga Mario, Campos-Roca Yolanda, Pérez Carlos J.

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1039--1056

EN

Automatic voice condition analysis systems have been developed to automatically discriminate pathological voices from healthy ones in the context of two disorders related to exudative lesions of Reinke’s space: nodules and Reinke’s edema. The systems are based on acoustic features, extracted from sustained vowel recordings. Reduced subsets of features have been obtained from a larger set by a feature selection algorithm based on Whale Optimization in combination with Support Vector Machine classification. Robustness of the proposed systems is assessed by adding noise of two different types (synthetic white noise and actual noise recorded in a clinical environment) to corrupt the speech signals. Two speech databases were used for this investigation: the Massachusetts Eye and Ear Infirmary (MEEI) database and a second one specifically collected in Hospital San Pedro de Alcántara (Cáceres, Spain) for the scope of this work (UEX-Voice database). The results show that the prediction performance of the detection systems appreciably decrease when moving from MEEI to a database recorded in more realistic conditions. For both pathologies, the prediction performance declines under noisy conditions, being the effect of white noise more pronounced than the effect of noise recorded in the clinical environment.

7

Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: A machine learning based approach

Kassani Sara Hosseinzadeh, Kassani Peyman Hosseinzadeh, Wesolowski Michal J., Schneider Kevin A., Deters Ralph

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

867--879

EN

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learningbased feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

8

Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images

Zhao Xinxing, Ang Candice Ke En, Acharya U. Rajendra, Cheong Kang Hao

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

456--473

EN

Alzheimer’s disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. It is one of the leading types of dementia for persons aged above 65 worldwide. In order to achieve accurate and timely diagnosis, and for detection of AD in its early stages, numerous Artificial Intelligence (AI) based Computer-aided Diagnostic (CAD) approaches have been proposed using data from brain imaging. In this paper, we review the recent application of AI based CAD systems on AD and its stages, with a particular focus on the use of structural MRI due to its cost effectiveness and lack of ionizing radiation. We will review important factors of different AI techniques pertinent to AD, summarize contributions from different research groups, critically discuss challenges involved and propose directions for future research. Ultimately, it would be ideal for development of a diagnostic framework that could be applicable to not only AD, but to different types of dementia as well in the future.

9

A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images

Saini Deepak, Chand Trilok, Chouhan Devendra K., Prakash Mahesh

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

419--444

EN

Objective: The purpose of present review paper is to introduce the reader to key directions of manual, semi-automatic and automatic knee osteoarthritis (OA) severity classification from plain radiographs. This is a narrative review article in which we have described recent developments in severity evaluation of knee OA from X-ray images. We have primarily focussed on automatic analysis and have reviewed articles in which machine learning, transfer learning, active learning, etc. have been employed on X-ray images to access and classify the severity of knee OA. Methods: All original research articles on OA detection and classification using X-ray images published in English were searched on PubMed database, Google Scholar, RSNA radiology databases in year 2019. The search terms of ‘‘knee Osteoarthritis” were combined with search terms ‘‘Machine Learning”, ‘severity” and ‘‘X-ray”. Results: The initial search on various publication databases revealed a total of 743 results, out of which only 26 articles were considered relevant to radiographic knee OA severity analysis. The majority of the articles were based on automatic analysis. Manual segmentation based articles were least in numbers. Conclusion: Computer aided methods to diagnose knee OA are great tools to detect OA at ealry stages. Advancements in Human Computer Interface systems have led the researchers to bridge the gap between machine learning algorithms and expert healthcare professionals to provide better and timely treatment options to the knee OA affected patients.

10

Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network

Ramirez-Bautista Julian Andres, Huerta-Ruelas Jorge Adalberto, Kóczy László T., Hatwágner Miklós F., Chaparro-Cárdenas Silvia L., Hernández-Zavala Antonio

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

404--414

EN

Load distribution analysis on foot surface allows knowing human mechanical behavior and aids the doctor in the detection of gait disorders like, the risk of foot ulcerations, leg discrepancy, and footprint alterations. Plantar pressure data combined with techniques that use integral reasoning produce easy understanding medical tools for assisting in treatment, early detection, and the development of preventive strategies. The present research compares the classification of human plantar foot alterations using Fuzzy Cognitive Maps (FCM) trained by Genetic Algorithm (GA) against a Multi-Layer Perceptron Neural Network (MLPNN). One hundred and fifty-one subject volunteers (aged 7–77) were classified previously with the flat foot (n = 70) and cavus foot (n = 81) by specialized physicians of the Piédica diagnostic center. The trial walking was conducted using plantar pressure platforms FreeMed®. The foot surface was divided into 14 areas that included toe 1 st to 5th, metatarsal joint 1st to 5th, lateral midfoot, medial midfoot, lateral heel, and medial heel. Pressure data were normalized for each area. Better performance in the classification using small amounts of data were found by using Fuzzy rather than non-Fuzzy approach.

11

A novel privacy-supporting 2-class classification technique for brain MRI images

Devi Swagatika, Sahoo Manmath Narayan, Bakshi Sambit

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1022--1035

EN

Developing automated Computer Aided Diagnosis (CAD) framework for assisting radiologists in a fast and effective classification of brain Magnetic Resonance (MR) images is of great importance, given plausible usage of Electronic Health Records (EHR) in healthcare system. This work aims at proposing two novel privacy supporting classifiers for automatic segregation of brain MR images. To ensure privacy, our article employs a spatial steganographic approach to hide patients sensitive health information in brain images itself. Proposed methods employ Discrete Wavelet Transform (DWT) for extracting relevant features from original and stego images. Subsequently, Symmetrical Uncertainty Ranking (SUR) and Probabilistic Principal Components Analysis (PPCA) are used to obtain a reduced feature vector for Support Vector Machine (SVM) and Filtered Classifier (FC) respectively. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate this work, the proposed schemes are experimented on both original and stego brain MR images and are compared against eight state-of-the-art classification techniques with respect to six standard parameters. The results reveal that the proposed techniques are robust and scalable with respect to the size of the datasets. Moreover, it is concluded that applying steganographic algorithm on brain MR images yield equally satisfactory classification rate.

12

A deep learning approach to detect Covid-19 coronavirus with X-Ray images

Jain Govardhan, Mittal Deepti, Thakur Daksh, Mittal Madhup K.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1391--1405

EN

Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5- fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/ sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.

13

Automatic method for assessment of proliferation index in digital images of DLBCL tissue section

Gomolka Ryszard S., Korzynska Anna, Siemion Krzysztof, Gabor-Siatkowska Karolina, Klonowski Wlodzimierz

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

30--37

EN

Diffuse large B-cell lymphoma (DLBCL) is a fast-growing and aggressive neoplasm originating from B lymphocytes. Evaluation of proliferation index (PI) based on Ki67 immunohistochemical nuclear staining is used to distinguish proliferating (immunopositive) from nonproliferating (immunonegative) lymphoma cells. Human interpretation of PI varies and is time-consuming, therefore automatic computer-assisted approach may facilitate the performance. Herein we propose a new fully automatic proliferation index estimation (FLAPIE) algorithm, dedicated to detection of immunopositive and immunonegative nuclei, and evaluation of PI in digital microscopy images of DAB&H-stained samples from patients with high-grade DLBCL. FLAPIE performs nuclei detection in original RGB colour space and is independent of image brightness due to its textural-statistical approach. Validation of FLAPIE was performed in 61 non-overlapping whole-slide imagefragments and compared to the results of PI estimation by QuPath open-source software, MetPiKi algorithm and manual evaluation by two independent observers. Interobserver agreement was calculated between the nuclei count and PIs by two observers. High concordance was found between both DAB and H-stained nuclei count, and PIs by two observers. Compared to MetPiKi, FLAPIE presented improved results of DAB and H-stained nuclei detection. In contrary to MetPiKi and QuPath, FLAPIE performed nuclei detection in all images and its results closely matched the number of DAB-stained nuclei evaluated by two observers. No significant difference was found between PIs by all computational methods and observers. FLAPIE achieved good results in PI estimation and prospectively aims to serve as a tool for clinical application in support of patients selection and decision to treatment.

14

Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images

Alirezazadeh P., Hejrati B., Monsef-Esfahani A., Fathi A.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

671--683

EN

Breast cancer has high incidence rate compared to the other cancers among women. This disease leads to die if it does not diagnosis early. Fortunately, by means of modern imaging procedure such as MRI, mammography, thermography, etc., and computer systems, it is possible to diagnose all kind of breast cancers in a short time. One type of BC images is histology images. They are obtained from the entire cut-off texture by use of digital cameras and contain invaluable information to diagnose malignant and benign lesions. Recently by requesting to use the digital workflow in surgical pathology, the diagnosis based on whole slide microscopy image analysis has attracted the attention of many researchers in medical image processing. Computer aided diagnosis (CAD) systems are developed to help pathologist make a better decision. There are some weaknesses in histology images based CAD systems in compared with radiology images based CAD systems. As these images are collected in different laboratory stages and from different samples, they have different distributions leading to mismatch of training (source) domain and test (target) domain. On the other hand, there is the great similarity between images of benign tumors with those of malignant. So if these images are analyzed undiscriminating, this leads to decrease classifier performance and recognition rate. In this research, a new representation learning-based unsupervised domain adaptation method is proposed to overcome these problems. This method attempts to distinguish benign extracted feature vectors from those of malignant ones by learning a domain invariant space as much as possible. This method achieved the average classification rate of 88.5% on BreaKHis dataset and increased 5.1% classification rate compared with basic methods and 1.25% with state-of-art methods.

15

Peripheral blood smear analysis using image processing approach for diagnostic purposes: A review

Hegde R. B., Prasad K., Hebbar H., Sandhya I.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

467--480

EN

Peripheral blood smear analysis is a common practice to evaluate health status of a person. Many disorders such as malaria, anemia, leukemia, thrombocytopenia, sickle cell anemia etc., can be diagnosed by evaluating blood cells. Many groups have reported methods to automate blood smear analysis for detection of specific disorders for diagnostic purposes. In this paper,we have summarized the methods used to analyze peripheral blood smears using image processing techniques. We have categorized these methods into three groups based on approaches such as WBC analysis, RBC analysis and platelet analysis. We conclude that there is a need for a method of automation to match with human evaluation process and rule out any abnormality present in the blood smear. It is desirable for studies on automation of peripheral blood smear analysis to focus on development of robust method to handle illumination and color shade variations. Also, it is desirable to design a method which could collect all the abnormal regions from all views of a specimen to limit the manual evaluation to those regions making it more feasible for telemedicine applications.

16

Novel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus images

Raghavendra U., Bhandary S. V., Gudigar A., Acharya U. R.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

170--180

EN

Glaucoma is the prime cause of blindness and early detection of it may prevent patients from vision loss. An expert system plays a vital role in glaucoma screening, which assist the ophthalmologists to make accurate decision. This paper proposes a novel technique for glaucoma detection using optic disk localization and non-parametric GIST descriptor. The method proposes a novel area based optic disk segmentation followed by the Radon transformation (RT). The change in the illumination levels of Radon transformed image are compensated using modified census transformation (MCT). The MCT images are then subjected to GIST descriptor to extract the spatial envelope energy spectrum. The obtained dimension of the GIST descriptor is reduced using locality sensitive discriminant analysis (LSDA) followed by various feature selection and ranking schemes. The ranked features are used to build an efficient classifier to detect glaucoma. Our system yielded a maximum accuracy (97.00%), sensitivity (97.80%) and specificity (95.80%) using support vector machine (SVM) classifier with nineteen features. Developed expert system also achieved maximum accuracy (93.62%), sensitivity (87.50%) and specificity (98.43%) for public dataset using twenty six features. The proposed method is efficient and computationally less expensive as it require only nineteen features to model a classifier for the huge dataset. Therefore the proposed method can be effectively utilized in hospitals for glaucoma screening.

17

Improvement in the diagnosis of melanoma and dysplastic lesions by introducing ABCD-PDT features and a hybrid classifier

Zakeri A., Hokmabadi A.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

456--466

EN

Melanoma and dysplastic lesions are pigmented skin lesions whose accurate classification is of great importance. In this paper, we have proposed a computer-aided diagnosis (CAD) system to improve the diagnostic ability of the conventional ABCD (asymmetry, border irregularity, color, and diameter) analysis. We introduced features extracted by local analysis of range of intensity variations within the lesion that describe pigment distribution and texture (PDT) features. The statistical distribution of pigmentation at a specified direction and distance was analyzed through grey level co-occurrence matrix (GLCM). Some other quantitative features were also extracted by computing neighborhood grey-tone difference matrix. These were correlated with human perception of texture. A hybrid classifier was designed for classification of melanoma, dysplastic, and benign lesions. Log-linearized Gaussian mixture neural network (LLGMNN), K-nearest neighborhood (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) construct the hybrid classifier. The proposed system was evaluated on a set of 792 dermoscopy images and the diagnostic accuracies of 96.8%, 97.3%, and 98.8% for melanoma, dysplastic, and benign lesions were achieved, respectively. The results indicate that PDT features are promising features which in combination with the conventional ABCD features are capable of enhancing the classification performance of the pigmented skin lesions.

18

Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis

Ibrahim S., Djemal R., Alsuwailem A.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

16--26

EN

Quantification of abnormality in brain signals may reveal brain conditions and pathologies. In this study, we investigate different electroencephalography (EEG) feature extraction and classification techniques to assist in the diagnosis of both epilepsy and autism spectrum disorder (ASD). First, the EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). Two nonlinear methods were studied, namely, Shannon entropy and largest Lyapunov exponent, which measure complexity and chaoticity in the EEG recording, in addition to the two conventional methods (namely, standard deviation and band power). We also study the use of a cross-correlation approach to measure synchronization between EEG channels, which may reveal abnormality in communication between brain regions. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design exploration techniques: the University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and our own EEG recordings (46 subjects). The combination of DWT, Shannon entropy, and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem. The proposed method obtained better classification accuracy compared to the existing methods and tested using larger and more comprehensive EEG dataset. The proposed method could potentially be used to assist epilepsy and ASD diagnosis therefore improving the speed and the accuracy.

19

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Faust O., Acharya U. R., Meiburger K. M., Molinari F., Koh J. E. W., Yeong C. H., Kongmebhol P., Ng K. H.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

275--296

EN

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a compet-itive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which deliver real benefits to patients by improving the diagnosis accuracy while reducing health care cost.

20

Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD

Bharath R., Mishra P. K., Rajalakshmi P.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

145--157

EN

Fatty liver is a prevalent disease and is the major cause for the dysfunction of the liver. If fatty liver is untreated, it may progress into chronic diseases like cirrhosis, hepatocellular carcinoma, liver cancer, etc. Early and accurate detection of fatty liver is crucial to prevent the fatty liver progressing into chronic diseases. Based on the severity of fat, the liver is categorized into four classes, namely Normal, Grade I, Grade II and Grade III respectively. Ultrasound scanning is the widely used imaging modality for diagnosing the fatty liver. The ultrasonic texture of liver parenchyma is specific to the severity of fat present in the liver and hence we formulated the quantification of fatty liver as a texture discrimination problem. In this paper, we propose a novel algorithm to discriminate the texture of fatty liver based on curvelet transform and SVD. Initially, the texture image is decomposed into sub-band images with curvelet transform enhancing gradients and curves in the texture, then an absolute mean of the singular values are extracted from each curvelet decomposed image, and used it as a feature representation for the texture. Finally, a cubic SVM classifier is used to classify the texture based on the extracted features. Tested on a database of 1000 image textures with 250 image textures belonging to each class, the proposed algorithm gave an accuracy of 96.9% in classifying the four grades of fat in the liver.