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Author

A. Jayachandran

Bio: A. Jayachandran is an academic researcher from Presidency University, Kolkata. The author has contributed to research in topics: Feature extraction & Segmentation. The author has an hindex of 9, co-authored 18 publications receiving 202 citations.

Papers
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Journal ArticleDOI
TL;DR: The obtained results depict that the proposed Multi‐texton histogram and support vector machine based brain tumor detection approach is more robust than the other classifiers in terms of sensitivity, specificity, and accuracy.
Abstract: Segmentation is the process of labeling objects in image data. It is a decisive phase in several medical imaging processing tasks for operation planning, radio therapy or diagnostics, and widely useful for studying the differences of healthy persons and persons with tumor. Magnetic Resonance Imaging brain tumor segmentation is a complicated task due to the variance and intricacy of tumors. In this article, a tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. Our proposed method hits the target with the aid of the following major steps: (i) Tumor Region Location, (ii) Feature Extraction using Multi-texton Technique, and (iii) Final Classification using support vector machine (SVM). The results for the tumor detection are validated through evaluation metrics such as, sensitivity, specificity, and accuracy. The comparative analysis is carried out by Radial Basis Function neural network and Feed Forward Neural Network. The obtained results depict that the proposed Multi-texton histogram and support vector machine based brain tumor detection approach is more robust than the other classifiers in terms of sensitivity, specificity, and accuracy. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 97–103, 2013

33 citations

Journal ArticleDOI
TL;DR: The overall classification accuracy of the proposedHTF with MCNNs is 98.41%, but the existing methods HTF with SVM and HTFwith CNNs produce 97.84% and 96.65% respectively.

32 citations

Journal ArticleDOI
TL;DR: The proposed multi-class brain tumor classification system comprises feature extraction and classification, and the fuzzy logic-based hybrid kernel is designed and applied to train the support vector machine for automatic classification of four different types of brain tumors.
Abstract: Image classification is one of the typical computational applications widely used in the medical field, especially for abnormality detection in magnetic resonance (MR) brain images. Medical image classification is a pattern recognition technique in which different images are categorized into several groups based on some similarity measures. One of the significant applications is the tumor type identification in abnormal MR brain images. The proposed multi-class brain tumor classification system comprises feature extraction and classification. In feature extraction, the attributes of the co-occurrence matrix and the histogram are represented within the feature vector. In this work, the advantage of both co-occurrence matrix and histogram to extract the texture feature from every segment is used for better classification. In classification, the fuzzy logic-based hybrid kernel is designed and applied to train the support vector machine for automatic classification of four different types of brain tumors such as Meningioma, Glioma, Astrocytoma, and Metastases. Based on the experimental results, the proposed brain tumor classification method is more robust than other traditional methods in terms of the evaluation metrics, sensitivity, specificity, and accuracy.

30 citations

Journal ArticleDOI
TL;DR: A robust brain tumor classification method is proposed, which focuses on the structural analysis on both tumorous and normal tissues, and is good at detecting the tumors in the brain MRI images.
Abstract: Image segmentation is to recognize structures in the image that are expected to signify scene objects. It is widely used by the radiologists to segment the medical images into meaningful regions. Thus, various segmentation techniques in medical imaging depending on the region of interest had been proposed. In this article, a robust brain tumor classification method is proposed, which focuses on the structural analysis on both tumorous and normal tissues. The proposed system consists of preprocessing, segmentation, feature extraction and classification. In preprocessing steps, anisotropic filter is used to eliminate the noise and enhances the image quality for skull-stripping process. In feature extraction, some specific features are extracted using texture as well from intensity using modified multi-texton structure descriptor. The hybrid kernel is designed in the classification stage and applied to training of support vector machine to perform automatic classification of tumor in magnetic resonance imaging (MRI) images. For comparative analysis, the proposed method is compared with the existing works using k-fold cross-validation method. The accuracy level (93 %) for our proposed approach (αK1, K1 + K2, K1 * K2) proved is good at detecting the tumors in the brain MRI images.

29 citations

Journal ArticleDOI
TL;DR: A hybrid algorithm for detection brain tumor in Magnetic Resonance images using statistical features and Fuzzy Support Vector Machine (FSVM) classifier and the result shows that the proposed technique is robust and effective compared with other recent works.
Abstract: In this study we have proposed a hybrid algorithm for detection brain tumor in Magnetic Resonance images using statistical features and Fuzzy Support Vector Machine (FSVM) classifier. Brain tumors are not diagnosed early and cured properly so they will cause permanent brain damage or death to patients. Tumor position and size are important for successful treatment. There are several algorithms are developed for brain tumor detection and classifications in the field of medical image processing. The proposed technique consists of four stages namely, Noise reduction, Feature extraction, Feature reduction and Classification. In the first stage anisotropic filter is applied for noise reduction and to make the image suitable for extracting features. In the second stage, obtains the texture features related to MRI images. In the third stage, the features of magnetic resonance images have been reduced using principles component analysis to the most essential features. At the last stage, the Supervisor classifier based FSVM has been used to classify subjects as normal and abnormal brain MR images. Classification accuracy 95.80% has been obtained by the proposed algorithm. The result shows that the proposed technique is robust and effective compared with other recent works.

28 citations


Cited by
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Journal ArticleDOI
TL;DR: In the article “Practice parameter: Evidence-based guidelines for migraine headache (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy …
Abstract: In the article “Practice parameter: Evidence-based guidelines for migraine headache (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy …

384 citations

Journal ArticleDOI
TL;DR: The developed method is effective and can be used in computer-aided systems to detect brain tumor and the model developed with the highest performance has classified the brain tumor images.

187 citations

Journal ArticleDOI
TL;DR: This paper attempts to solve the problem of maintaining diversity among wide image specifications by optimizing the threshold by introducing a hybrid framework of Artificial Bee Colony and Genetic Algorithm in a region growing variant, in which gradient and intensity levels are used for segmentation.
Abstract: In recent decades, region growing methods in image segmentation plays a vital role in medical image processing. Nonetheless, the method needs more advancement to cope up with the images of current acquisition devices. This paper attempts to solve the problem of maintaining diversity among wide image specifications by optimizing the threshold. In order to accomplish this, we introduce a hybrid framework of Artificial Bee Colony and Genetic Algorithm in a region growing variant, in which gradient and intensity levels are used for segmentation. Eventually, the proposed work is subjected to classify the tumor and non-tumor images, followed by the segmentation of tumor region in MRI images. Classification methodologies such as feed forward back propagation neural network, radial basis neural network, support vector machine with quadratic programming and adaptive neuro-fuzzy inference system are considered for experimental investigation in which support vector machine with quadratic programming is found to be dominant than other methodologies. Proposed region growing method outperforms well on the classified image, when compared with the region growing variant and standard region growing method. The results are demonstrated with the aid of wide set of performance measures. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 129-137, 2014

181 citations

Journal ArticleDOI
TL;DR: The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method.
Abstract: A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.

170 citations

Journal ArticleDOI
TL;DR: This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis and identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes.

147 citations