Shaswati Roy

RCC Institute of Information Technology · Deaprtment of Information Technology

In cancer research, automatic brain tumor detection from 3-D magnetic resonance (MR) images is an important pre-requisite. In this regard, the paper presents a new method for segmentation of brain tumor from MR volumes corrupted with different imaging artifact, such as bias field and noise. It addresses the problems of uncertainty and bias field ar...

An important diagnostic technique for providing accurate information about the spatial distribution of brain soft tissues non-invasively is magnetic resonance (MR) imaging. In MR images, different imaging artifacts give rise to uncertainties in brain volume segmentation into major soft tissue classes; as well as in extracting brain tumor and evalua...

Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist, a pre-operative method for determining tumor malignancy state still remains as an active research...

Image segmentation is an important pre-requisite step for any automatic clinical analysis technique. It assists in visualization of human tissues, as accurate delineation of medical images requires involvement of expert practitioners, which is also time consuming. In this background, the rough-fuzzy clustering algorithm provides an effective approa...

Segmentation of brain MR volumes into different meaningful tissue classes is an essential prerequisite for many clinical analyses. However, intensity inhomogeneity or bias field, present in MR volumes, considerably degrades the quality of segmentation. In this regard, the paper presents a new segmentation algorithm, termed as CoLoRS (Coherent Local...

Segmentation of brain region from an MR volume is an essential prerequisite for any automatic medical image processing application as it increases both speed and accuracy of the diagnosis in manifold. Due to material heterogeneity and resolution limitation of imaging devices, the MR image introduces graded intensity of tissues within the brain regi...

One of the important problems in medical diagnosis is the segmentation and detection of brain tumor in MR images. The accurate estimation of brain tumor size is important for treatment planning and therapy evaluation. In this regard, this paper presents a new method, termed as SoBT-RFW, for segmentation of brain tumor fromMR images. It integrates j...

Automatic and accurate brain tumor segmentation from MR images is one of the important problems in cancer research. However, the lack of shape prior and weak contrast at boundary make unsupervised brain tumor segmentation more challenging. In this background, a new brain tumor segmentation method is being developed, integrating judiciously the meri...

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper pre...

The skull stripping method is an important area of study in brain image processing applications. It acts as preliminary step in numerous medical applications as it increases speed and accuracy of diagnosis in manifold. It removes non-cerebral tissues like skull, scalp, and dura from brain images. In this regard, a simple skull stripping algorithm,...

This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions. It assumes that the text and non-text or graphics regions of a given document are considered to have different textural properties. The M-band wavel...

This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions. It assumes that the text and non-text regions of a given document are considered to have different textural properties. The M-band wavelet packet is...