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1

Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE

Hashemzehi Raheleh, Mahdavi Seyyed Javad Seyyed, Kheirabadi Maryam, Kamel Seyed Reza

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1225--1232

EN

A brain tumor is an abnormal growth of cells inside the skull. Malignant brain tumors are among the most dreadful types of cancer with direct consequences such as cognitive decline and poor quality of life. Analyzing magnetic resonance imaging (MRI) is a popular technique for brain tumor detection. In this paper, we use these images to train our new hybrid paradigm which consists of a neural autoregressive distribution estimation (NADE) and a convolutional neural network (CNN). We subsequently test this model with 3064 T1-weight-ed contrast-enhanced images with three types of brain tumors. The results demonstrate that the hybrid CNN-NADE has a high classification performance as regards the availability of medical images are limited.

2

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey Nilanjan, Rajinikanth V., Shi Fuqian, Tavares João Manuel R.S., Moraru Luminita, Arvind Karthik K., Lin Hong, Kamalanand K., Emmanuel C.

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 3

843--856

EN

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance- Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation.

3

Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms

Kabir Anaraki Amin, Ayati Moosa, Kazemi Foad

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

63--74

EN

Gliomas are the most common type of primary brain tumors in adults and their early detection is of great importance. In this paper, a method based on convolutional neural networks (CNNs) and genetic algorithm (GA) is proposed in order to noninvasively classify different grades of Glioma using magnetic resonance imaging (MRI). In the proposed method, the architecture (structure) of the CNN is evolved using GA, unlike existing methods of selecting a deep neural network architecture which are usually based on trial and error or by adopting predefined common structures. Furthermore, to decrease the variance of prediction error, bagging as an ensemble algorithm is utilized on the best model evolved by the GA. To briefly mention the results, in one case study, 90.9 percent accuracy for classifying three Glioma grades was obtained. In another case study, Glioma, Meningioma, and Pituitary tumor types were classified with 94.2 percent accuracy. The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images. Due to the flexible nature of the method, it can be readily used in practice for assisting the doctor to diagnose brain tumors in an early stage.

4

Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering

Raju A. R., Suresh P., Rao R. R.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

646--660

EN

Brain tumor segmentation and classification is the interesting area for differentiating the tumerous and the non-tumerous cells in the brain and to classify the tumerous cells for identifying its level. The conventional methods lack the automatic classification and they consumed huge time and are ineffective in decision-making. To overcome the challenges faced by the conventional methods, this paper proposes the automatic method of classification using the Harmony-Crow Search (HCS) Optimization algorithm to train the multi-SVNN classifier. The brain tumor segmentation is performed using the Bayesian fuzzy clustering approach, whereas the tumor classification is done using the proposed HCS Optimization algorithm-based multi-SVNN classifier. The proposed method of classification determines the level of the brain tumor using the features of the segments generated based on Bayesian fuzzy clustering. The robust features are obtained using the information theoretic measures, scattering transform, and wavelet transform. The experimentation performed using the BRATS database conveys proves the effectiveness of the proposed method and the proposed HCS-based tumor segmentation and classification achieves the classification accuracy of 0.93 and outperforms the existing segmentation methods.

5

Śródoperacyjna metoda obrazowania w podczerwieni ukrwienia tkanek prawidłowych i patologicznych mózgu

Kaczmarska K., Czernicki Z, Zębala M, Kastek M., Piątkowski T., Polakowski H.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 9

38-41

PL

Śródoperacyjna identyfikacja tkanek mózgu objętych procesem chorobowym, precyzyjne dotarcie do nich oraz radykalne ich usunięcie stanowi istotę każdego zabiegu neurochirurgicznego. Celem podjętych badań jest ocena przydatności kamery termowizyjnej do identyfikacji i wyznaczania lokalizacji guzów mózgu oraz opracowanie metodologii obrazowania śródoperacyjnego umożliwiającego analizę zmian temperatury powierzchni kory mózgowej. Analizę termiczną przeprowadzono u sześciu pacjentów z wykrytymi w badaniach obrazowych nowotworami ośrodkowego układu nerwowego. Wyniki pomiarów przeprowadzonych śródoperacyjnie potwierdzają możliwość wykorzystania kamery termowizyjnej do nieinwazyjnej rejestracji temperatury powierzchni kory mózgowej oraz wskazują, że temperatura powierzchni guzów różni się od temperatury powierzchni tkanek nieobjętych zmianą chorobową.

EN

Intraoperative identification of brain tissues affected by the disease, precise access to them and radical removal of them are essence of neurosurgery. The main goal of this work is evaluate the usefulness of intraoperative thermal imaging to determine the location and borders of brain tumors. Moreover, it is important to develop a methodology for intraoperative imaging that allows analysis of the temperature changes of cerebral cortex surface. Preliminary clinical trials were carried out on six patients with tumors of central nervous system diagnosed with magnetic resonance imaging or computed tomography. The results of intraoperative measurements confirm the possibility of using infrared camera for non-invasive record of the temperature distribution of the cerebral cortex surface. The results showed significant differences in temperature of healthy tissues of brain and temperature of tumors.

6

Texture Analysis for 3D Classification of Brain Tumor Tissues

Arunadevi B., Deepa S.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 4

338-342

EN

This paper investigates on extending and comparing the Gray level co-occurrence matrices (GLCM) and 3D Gabor filters in volumetric texture analysis of brain tumor tissue classification. The extracted features are sub-selected by genetic algorithm for dimensionality reduction and fed into Extreme Learning Machine Classifier. The organizational prototype of image voxels distinctive to the underlying substrates in a tissue is been evaluated and validated on public and clinical dataset revealing 3D GLCM more appropriate towards brain tumor tissue classification.

PL

W artykule zbadano i porównano algorytmy klasyfikacji tkanki guza mózgu – GLCM i filtry Gabora 3D. Właściwości ekstrakcji były selekcjonowane przy użyciu algorytmu genetycznego i klasyfikatora ELM.

7

Application of region growing method to brain tumor segmentation - preliminary results

Piekar E., Szwarc P., Sobotnicki A., Momot M.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

153--160

EN

In this article image have been subject to segmentation using Matlab software, i.e. T1 in normal conditions, perfusion images and images after administering a contrast agent. The tumor in images made in normal conditions was difficult to identify. The images obtained after administering the contrast agent confirmed that the homogeneity criterion has been appropriately selected. In perfusion images the pixels of the background were added to the tumor. When the parameters were changed i.e. pixel counter or neighborhood type the method became more efficient; the tumor boundaries were outlined more precisely. The region growing method enables precise tumor detection; however, the selection of an appropriate homogeneity criterion is a prerequisite for correct segmentation.

8

Synteza biokompatybilnych kopolimerów i ich zastosowanie w kontrolowanym uwalnianiu sterydów i analogów nukleozydów

Bero M., Kasperczyk J., Dobrzyński P., Grieb P., Kryczka T., Ryba M., Buntner B., Nowak M.

Chemik

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2004

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Vol. 57, nr 2

49-54

PL

Publikacja jest przeglądem naszych wyników dotyczących syntezy i zastosowania biodegradowalnych i biokompatybilnych kopolimerów w procesie kontrolowanego uwalniania leków. Przedstawiono nowe metody syntezy kopolimerów, bez zastosowania toksycznych inicjatorów zawierających cynę. Stwierdzono możliwość uzyskania stałej szybkości uwalniania leków takich jak nukleozydy i sterydy.

EN

This publication is a review of our results about synthesis and application of biodegradable and biocompatible copolymers used in the controlled drug release process. New methods of copolymers synthesis without toxic tin compounds initiators were presented. Application of obtained copolymers allow to release nucleoside analogs and some steroids at constant speed.