Atiq Islam

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The phenomenal growth of Image/Video on the web and the increasing sparseness of meta information to go along with forces us to look for signals from the Image/Video content for Search / Information Retrieval and Browsing based corpus exploration. One of the prominent type of information that users look for while searching/browsing through such corpora is(More)
A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a(More)
Background Automatic detection of tumors is a challenging task due to the heterogeneous phenotypic and genotypic behaviors of cells within tumor types [1-3]. In recent years, a number of research endeavors have been reported in lit-eratures that exploit microarray gene expression data to predict tissue/tumor types with high confidence [3-14]. However, in(More)
INTRODUCTION Automated diagnosis and prognosis of tumors of the central nervous system (CNS) offer overwhelming challenges because of heterogeneous phenotype and genotype behavior of tumor cells (Yang et al. 2003, Pomeroy et al. 2002). Unambiguous characterization of these tumors is essential for accurate prognosis and therapy. Although the present imaging(More)
INTRODUCTION In this chapter, we propose a novel algorithm for characterizing a variety of CNS tumors. The proposed algorithm is illustrated with an analysis of an Affyme-trix gene expression data from CNS tumor samples (Pomeroy et al., 2002). As discussed in the previous chapter entitled: CNS Tumor Prediction Using Gene Expression Data Part I, we used an(More)
—Based on microarray gene expression datasets, many statistical methods have been proposed to locate the significant differentially expressed genes (marker genes) among different sample groups. Although robust models for identifying marker genes more accurately is an area of intense research, effective tools for the evaluation of results is often ignored in(More)
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