Leena Silvoster M

Scopus Publications

Watershed based algorithms for the segmentation of spine MRI
M. Leena Silvoster, R. Mathusoothana, and S. Kumar
Springer Science and Business Media LLC

CNN: A review of models, application of IVD segmentation
Silvoster M. Leena, R. Mathusoothana, and S. Kumar
Wiley

Segmentation of Images Using Watershed and MSER: A State-of-the-Art Review
M. Leena Silvoster and R. Mathusoothana S. Kumar
Springer International Publishing

Efficient segmentation of lumbar intervertebral disc from MR images
Leena Silvoster M and Retnaswami Mathusoothana S. Kumar
Institution of Engineering and Technology (IET)
: Segmentation of spine Magnetic Resonance Images (MRIs) has become an indispensable process in the diagnosis of lumbar disc degeneration, causing low back pain. Over the last decade of years, computer-directed diagnosis of disease, as well as computer-guided spine surgery, is based on the two-dimensional (2D) analysis of mid-sagittal slice of MRI. This work proposes an automatic strategy to extract the 3D segmentation of the normal disc as well as degenerated lumbar intervertebral discs (IVDs) from T2-weighted Turbo Spin Echo MRI of the spine using Connected Component (CC) analysis algorithm and statistical shape analysis. The challenges faced by the IVD segmentation includes (i) partial volume effects (ii) intensity inhomogeneity (iii) grey level overlap of different soft tissues. The proposed method first pre-processes the dataset and enables it for the application of the CC algorithm. The CC (subsets of pixels of the disc) of the spine MRI is extracted and apply statistical shape analysis for the refinement of the segmentation results to detect IVDs. Experimental results of the proposed method show a robust segmentation, accomplishing the dice similarity index of 92.4% and thus achieving a low error rate. Other performance measures such as Precision, Accuracy, JaccardIdx, JaccardDist, Global Consistency Error, Variation of Information, etc were also evaluated. The algorithm is evaluated quantitatively using adequate experiments on a dataset of 15 MRI scans, of different scenarios such as healthy and degenerate disc and this proposed method is verified as a promising accurate method for the automatic segmentation of IVD

Automatic segmentation, size determination and classification of IVD

Efficient identification of desiccated lumbar IVD from MRI

Graph colouring based image similarity
M Leena Silvoster
IEEE
Region based segmentation approaches have been applied to several different problems in computer vision, like image classification, image registration, image retrieval, objects recognition, video indexing etc. The attraction of this algorithm is independent of the user defined seed point. The generality of region based segmentation of real-world problems in the light of regions has made it popular for large range of images. However in the prior methods, the computational effort increases as the complexity of the problem increases. This paper is based on an efficient graph theoretical approach. A maximal planar graph is constructed from the region segmented image. Primary advantage of this approach is one can naively analyze the resulting graph and determine the number and position of a particular object in the region of interest. The images are matched (with N points) in O(N2 log N) and segment a given image of N pixels in O(N log N).

Enhanced CNN based electron microscopy image segmentation
M. Leena Silvoster and V. K. Govindan
Walter de Gruyter GmbH
Abstract Detecting the neural processes like axons and dendrites needs high quality SEM images. This paper proposes an approach using perceptual grouping via a graph cut and its combinations with Convolutional Neural Network (CNN) to achieve improved segmentation of SEM images. Experimental results demonstrate improved computational efficiency with linear running time.

Convolutional neural network based segmentation
Leena Silvoster M. and Govindan V.K.
Springer Berlin Heidelberg