A new rectangular window based image cropping method for generalization of brain neoplasm classification systems

Citations Over TimeTop 16% of 2017 papers

Abstract

Abstract Classification of brain neoplasm images is one of the most challenging research areas in the field of medical image processing. The main objective of this study is to design a brain neoplasm classification system that can be trained using multiple various sized MR images of different institutions. The proposed method is a generalized classification system; it can be used in a single institute or in a number of institutions at the same time, without any restriction on image size. The generalization and unbiased capability of the proposed method can bring researchers on a single platform to work on some standard forms of computer aided diagnosis system with more efficient diagnostic capabilities. In this study, a suitable size of moveable rectangular window is used between segmentation and feature extraction stages. A semiautomatic, localized region based active contour method is used for segmentation of brain neoplasm region. Discrete wavelet transform for feature extraction, principal component analysis for feature selection and support vector machine is used as classifier. For the first time MR images of 2 sizes and from different institutions are used in training and testing of brain neoplasm classifier. Three glioma grades were classified using 92 MR images. The proposed method achieved the highest accuracy of 88.26%, the highest sensitivity of 92.23% and the maximum specificity of 93.93%. In addition, the proposed method is computationally less complex, requires shorter processing time and is more efficient in terms of storage capacity.

Related Papers

• → Classification of Vegetable Oils by Principal Component Analysis of FTIR Spectra(2003)57 cited
• → Use of Sparse Principal Component Analysis (SPCA) for Fault Detection(2016)15 cited
• → Principal Component Analysis Versus Factor Analysis(2020)3 cited
• → Fuzzy Clustering based Principal Component Analysis(2006)3 cited
• → Principal Component Analysis versus Factor Analysis(2021)