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1

Gray-level co-occurrence matrix of Fourier synchrosqueezed transform for epileptic seizure detection

Mamli Shamzin, Kalbkhani Hashem

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

87--99

EN

Epilepsy is a brain disorder that many persons of different ages in the world suffer from it. According to the world health organization, epilepsy is characterized by repetitive seizures and more electrical discharge in a group of brain neurons results in sudden physical actions. The aim of this paper is to introduce a new method to classify epileptic phases based on Fourier synchro-squeezed transform (FSST) of electroencephalogram (EEG) signals. FSST is a time-frequency (TF) analysis and provides sharper TF estimates than the conventional short-time Fourier transform (STFT). Absolute of FSST of EEG signal is computed and segmented into five non-overlapping frequency sub-bands as delta (d), theta (u), alpha (a), beta (b), and gamma (g). Each sub-band is considered as a gray-scale image and then we propose to obtain the gray-level co-occurrence matrix (GLCM) of each sub-band as features. We concatenate the features of different sub-bands to obtain the final feature vector. After selecting informative features by infinite latent feature selection (ILFS) method, the support vector machine (SVM) and K-nearest neighbor (KNN) classifiers are used separately to classify EEG signals. We use the EEG signals from Bonn University database and different combinations of its sets are considered. Simulation results show that the proposed method efficiently classifies the EEG signals and can be used to determine the phase of epilepsy.

2

Cerebral edema segmentation using textural feature

Chaudhari Archana, Kulkarni Jayant

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 3

599--612

EN

Diagnostic imaging provides a vital tool in detection and analysis of Brain pathologies. Magnetic resonance imaging (MRI) provides an effective means for non-invasive mapping of anatomy and pathology in the brain. Pathologies like cerebral edema and tumors can spread in different tissues in the brain and can affect cognitive and other functions in the body. Accurate segmentation is therefore a challenging task. Human Brain consists of different soft tissues. These tissues can be characterized using different textures. The work presents an automatic method for segmentation using textural feature of the MR image. The texture of MR image is exploited using the gray co-occurrence matrix (GLCM). GLCM creates a textural feature map by taking into account the spatial dependence of the pixels and its angular relationship between the neighboring cell pairs. Local entropy as second order textural feature is used to capture the texture of MR image. Entropy computes the randomness in pixel intensities and helps in defining a unique texture of edema for segmentation. The marked contrast enhancement obtained in FLAIR sequence of the MR image is captured as textural information by local entropy and GLCM combination. The proposed method obtains a definite textural signature of edema as well as tumor for threshold selection. Experiments on publically available BRATS database yields an average accuracy of 96%, specificity of 97%, sensitivity of 61%, Dice Coefficient as 50% and structural similarity index of 0.88 for edema. The proposed method demonstrates encouraging results in automatic segmentation of edema as well as tumor core.

3

A classification framework for prediction of breast density using an ensemble of neural network classifiers

Kumar I., Bhadauria H. S., Virmani J., Thakur S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

217--228

EN

The present work proposes a classification framework for the prediction of breast density using an ensemble of neural network classifiers. Expert radiologists, visualize the textural characteristics of center region of a breast to distinguish between different breast density classes. Accordingly, ROIs of fixed size are cropped from the center location of the breast tissue and GLCM mean features are computed for each ROI by varying interpixel distance 'd' from 1 to 15. The proposed classification framework consists of two stages, (a) first stage: this stage consists of a single 4-class neural network classifier NN0 (B-I/B-II/B-III/B-IV) which yields the output probability vector [PB-I PB-II PB-III PB-IV] indicating the probability values with which a test ROI belongs to a particular breast density class. (b) second stage: this stage consists of an ensemble of six binary neural network classifiers NN1 (B-I/B-II), NN2 (B-I/B-III), NN3 (B-I/B-IV), NN4 (B-II/B-III), NN5 (B-II/B-IV) and NN6 (B-III/B-IV). The output of the first stage of the classification framework, i.e. output on NN0 is used to obtain the two most probable classes for a test ROI. In the second stage this test ROI is passed through one of the binary neural networks, i.e. NN1 to NN6 corresponding to the two most probable classes predicted by NN0. [...]

4

Metoda specyfikacji histogramu wykorzystująca macierz współwystępowania i jej zastosowanie dla transformacji obrazu

Siuzdak J., Czarnecki T.

Kwartalnik Elektroniki i Telekomunikacji

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1998

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Vol. 44, nr 4

535-554

PL

W pracy zaprezentowano nową, globalną metodę specyfikacji histogramu obrazu monochromatycznego wykorzystującą własności macierzy współwystępowania. Opracowana metoda pozwala, w zależności od potrzeby, uwydatnić na obrazie większe szczegóły lub tekstury. Metoda może być również zastosowana do przekształcenia luminacji obrazów kolorowych.

EN

Presented is a method of histogram specification employing the cocurrence matrix properties. Its application to monochrome image transformations is discussed. The method enables one to enhance either greater details or texture depending on the requirements. The method may be adapted also for colour images.