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2 Biocybernetics and Biomedical Engineering

2 2016

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Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis

GeethaRamani R., Balasubramanian L.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 1

102--118

Most of the retinal diseases namely retinopathy, occlusion etc., can be identified through changes exhibited in retinal vasculature of fundus images. Thus, segmentation of retinal blood vessels aids in detecting the alterations and hence the disease. Manual segmentation of vessels requires expertise. It is a very tedious and time consuming task as vessels are only a few pixels wide and extend almost throughout entire span of the fundus image. Employing computational approaches for this purpose would help in efficient retinal analysis. The methodology proposed in this work involves sequential application of image pre-processing, supervised and unsupervised learning and image post-processing techniques. Image cropping, color transformation and color channel extraction, contrast enhancement, Gabor filtering and halfwave rectification are sequentially applied during pre-processing stage. A feature vector is formed from the pre-processed images. Principal component analysis is performed on the feature vector. K-means clustering is executed on this outcome to group pixels as either vessel or non-vessel cluster. Out of the two groups, the identified non-vessel group undergoes an ensemble classification process employing root guided decision tree with bagging, while vessel group is left unprocessed as further processing might increase misclassifications of vessels as non-vessels. The resultant segmented image is formed through combining the results of clustering and ensemble classification process. The vessel segmented output from previous phase is post-processed through morphological techniques. The proposed technique is validated on images from publicly available DRIVE database. The proposed methodology achieves an accuracy of 95.36%, which is comparable with the existing blood vessel segmentation techniques.

Altered modular organization in schizophrenia patients and analysis using supervised association rule mining

GeethaRamani R., Sivaselvi K.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 2

397--412

Complex neuro-degenerative disorders affect the intrinsic topological architecture of brain connectivity. There are very few studies concentrating on the occurrence of modular changes in the structural and functional connectome of people diagnosed with Schizophrenia. In this study, group averaged analysis on modular organization of 15 healthy and 12 Schizophrenic subjects were performed to understand the topological alterations occurring in brain networks of diseased against normal. The major contributing regions for changes in optimal brain architecture were also identified. It also involves the investigation of individual subject's functional connectivity and the attempts were made to extract the modular specific roles of brain regions through supervised association rule mining. On comparison with group average measurements, it was found to produce similar results and it was understood that inter and intra-module connections evidently varied in Schizophrenia because of alterations in extremely organized modular architecture. This is believed to provide new insights in understanding the complex neuro-degenerative disorder through analysis on modular organization of functional brain networks. Highly influential regions were also determined. These regions were found to be potential biomarkers for Schizophrenia diagnosis.