Qualification: 
M.Tech
k_raghesh@cb.amrita.edu

Raghesh Krishnan K. currently serves as Assistant Professor in the department of Computer science, Amrita School of Engineering, Coimbatore Campus. His areas of research include Image Processing and Biomedical Image Classification. .

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

2018

Journal Article

K. Raghesh Krishnan, Midhila, M., and R., S., “Tensor Flow Based Analysis and Classification of Liver Disorders from Ultrasonography Images”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 734-743, 2018.[Abstract]


In the field of medical imaging, Ultrasonography is a popular and most frequently used diagnostic tool owing to its hazard-free, non–invasive and the cost effective nature. Liver being the largest and vital organ in the human body, liver disorders are treated very important and initial detection of the disorder is made using ultrasound imaging by the radiologists that leads to additional biopsies for confirmation, if necessary. This work focusses on the automated classification of nine types of both focal and diffused liver disorders using ultrasound images. A deep convolutional neural network architecture codenamed Inception is used. The technique achieves a new state for classification and detection of liver disease. The disease is predicted based on the score obtained as a result of training. The classification is achieved using tensor flow and it outputs the predicted labels and the corresponding scores. The method achieves reasonable accuracy using the trained model.

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2017

Journal Article

K. Raghesh Krishnan and Radhakrishnan, S., “Hybrid approach to classification of focal and diffused liver disorders using ultrasound images with wavelets and texture features”, IET Image Processing, vol. 11, pp. 530-538, 2017.[Abstract]


This study presents a computer-based approach to classify ten different kinds of focal and diffused liver disorders using ultrasound images. The diseased portion is isolated from the ultrasound image by applying active contour segmentation technique. The segmented region is further decomposed into horizontal, vertical and diagonal component images by applying biorthogonal wavelet transform. From the above wavelet filtered component images, grey level run-length matrix features are extracted and classified using random forests by applying ten-fold cross-validation strategy. The results are compared with spatial feature extraction techniques such as intensity histogram, invariant moment features and spatial texture features such as grey-level co-occurrence matrices, grey-level run length matrices and fractal texture features. The proposed technique, which is an application of texture feature extraction on transform domain images, gives an overall classification accuracy of 91% for a combination of ten classes of similar looking diseases which is appreciable than the spatial domain only techniques for liver disease classification from ultrasound images. © The Institution of Engineering and Technology.

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2015

Journal Article

K. Raghesh Krishnan and Radhakrishnan, S., “Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation”, IET Image Processing, vol. 9, pp. 261-270, 2015.[Abstract]


Preliminary diagnosis based on ultrasound scanning is the first step in the treatment of many abdominal diseases. The noisy nature of the ultrasound image coupled with minimal contrasting features complicates the task of automatic classification if not impossible. This study presents a segmentation-based approach to automatic classification of ten types of diffused and focal liver diseases from ultrasound images. A novel approach using Isocontour Segmentation based on Marching Squares, a computer graphics algorithm is presented. GLCM and fractal features are extracted from the segmented ultrasound images and classified using support vector machines and artificial neural networks (ANN) and the results are analysed. An overall classification accuracy of 92% is achieved using fractal features and ANN. © The Institution of Engineering and Technology 2015.

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2014

Journal Article

M. Sarma, Somanath, A., and K. Raghesh Krishnan, “Processing and Interpreting Water bodies and Road Networks from Satellite Images and storing them as QR Codes”, International Journal of Scientific & Engineering Research, vol. 5, no. 5, pp. 1285 – 1291, 2014.[Abstract]


Study on remote sensing images is becoming an important target for geological survey activities, mineral exploration, industrial investment, etc. A detailed study and observation about an image is essential to classify every object in the image. This work aims to classify water bodies and road networks from satellite images. Satellite image analysis provides a statistically superior method of sampling that is not possible via conventional ground "grab sampling" methods. Satellite imaging can analyze the entire body of water and identify the areas that need treatment or can be used for planning purposes. However, heavy cloud obstruction or vegetation coverage will impact the ability to view and analyze water or land. Also, remotely sensed images may be severely affected due to different kinds of noise. In order to overcome this, initially, the image is checked for noise and is removed, if necessary using an iterative filtering algorithm for impulse noise removal. The denoising is followed by segmentation after which feature extraction or classification can be performed to identify the objects. After classification, the information is to be stored for future reference. The last step is to store back the information in the form of a QR code. Previously, barcodes were used for storing such information. However with the advent of smartphone technology, people are always connected to the internet and the new technology of QR codes is more suitable for this purpose. The information can be used f or various purposes like geographical surveys, mapping of regions and can also be used to find out the author or the person who has processed the image for any future clarifications.

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Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2017

Conference Proceedings

M. M. and K. Raghesh Krishnan, “A Study of the Phases of Classification of Liver diseases from Ultrasound Images and Gray Level Difference Weights based Segmentation”, IEEE Digital Library, 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, Chennai, India, 2017.[Abstract]


This paper presents a study of the state of the art techniques applied to computer based analysis and classification of liver diseases from ultrasound images. The diseased portions from the ultrasound images are analyzed and categorized using techniques such as Despeckling, Segmentation, Feature extraction and Classification. Automatic segmentation of ultrasound images is complicated due to the fact that the image may include other organs which are close to the liver, irregular structure of disease, poor quality of image, lack of color cues, and lack of definite boundaries and presence of noise. This work makes a study of different techniques used in the different phases of biomedical liver ultrasound processing such as noise removal, segmentation, Feature Extraction and classification. This work also presents the segmentation results obtained using Gray Level Difference Weights Method on 10 types of liver diseases from ultrasound images.

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2013

Conference Proceedings

K. Raghesh Krishnan and Sudhakar, R., “Automatic Classification of Liver Diseases from Ultrasound Images Using GLRLM Texture Features”, Advances in Intelligent Systems and Computing, vol. 195 AISC. pp. 611-624, 2013.[Abstract]


Ultrasound imaging is considered to be one of the most cost-effective and non-invasive techniques for conclusive diagnosis in some cases and preliminary diagnosis in others. Automatic liver tissue characterization and classification from ultrasonic scans have been for long, the concern of many researchers, and has been made possible today by the availability of the most powerful and cost effective computing facilities. Automatic diagnosis and classification systems are used both for quick and accurate diagnosis and as a second opinion tool for clarifications. This paper analyzes the effect of various linear, non linear and diffusion filters in improving the quality of the liver ultrasound images before proceeding to the subsequent phases of feature extraction and classification using Gray Level Run Length Matrix Features and Support Vector Machines respectively.

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2011

Conference Proceedings

R. Sudhakar, K. Raghesh Krishnan, and Muthukrishnan, S., “A Hybrid Approach to Content Based Image Retrieval using Visual Features and Textual Queries”, International Conference on Advanced Computing (ICOAC 2011) . IEEE, Chennai, India, 2011.[Abstract]


In the recent years, with an increase in the awareness of internet usage, there has been an explosion of data on the web. Huge amount of data resides on the web and of late there has been an increased necessity for search engines that retrieve documents and images, at least close to the search criteria if not exactly. The problem of retrieving near approximate images using textual queries has always been an area of research. This paper focuses on bridging the gap between textual search input given by the user and the images retrieved from the database, by making use of visual features instead of the file name, which is generally the case in many search engines.

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2011

Conference Proceedings

R. Aarthi, C., A., and K. Raghesh Krishnan, “Automatic Isolation and Classification of Vehicles in a Traffic Video”, Proceedings of the 2011 World Congress on Information and Communication Technologies (WICT 2011). Mumbai, India., pp. 357-361, 2011.