Measurement Extraction using Fuzzy Set Rule for Segmented Features of Brain Tumor in T-1 & T-2 Weighted Images

Abstract

Aims: For neuro radiologist it becomes hard to accumulate features with minute dissimilarity in plenty of cases, so it is hard to make a correct decision. Therefore, the need is to generate some rules for prediction of degree of malignancy in tumors. Design: The pre-operative analysis of brain lesion is based on magnetic resonance imaging and clinical data set. Analysis of MRI finding and medical data set gives the relationship between regular pattern & interpretable pattern to acquire desired degree of malignancy.  Until now the edge detection, segmentation and morphological operators are used to detect exact location of brain tumor. As uncertainty exits; here fuzzy set rules are evaluated to predict the degree by which a benign tumor is converted into malignant tumor. Methods: Fuzzy extraction theory has been applied along with image progressing algorithms like edge detection; segmentation and morphological operation based on spectral transformation are used to detect exact location of brain tumor to predict the degree malignancy. Step of Image analysis: a) Preprocessing: input 2D gif or tiff image b) Filtering of image using Anisodiff filter c) Thresholding, applying morphological operators and tumor line detection. Statistical Analysis used: A diagnostic feature includes blood flow, mass effect, temperature, calcification, edema, signal intensity & so on. Numerous features can be taken into consideration for better outcome. Results: Fuzzy set rule is one of the promising methods along with MR finding to achieve accuracy higher than 85% by considering few of the medical symptoms on different features. Conclusions: This research is limited to specific region and type of glioma and thus cannot deal heterogeneous cases in which situation is much complicated. The result evaluated here are usually retroactive. As studied, by analyzing signal intensity of T-1 & T-2 weighted image alone, accuracy of 60-70% has been achieved. So in order to get higher accuracy feature like cyst generation, oedema, blood supply are included to achieve 85% accuracy.