Texture Feature Based Analysis of Segmenting Soft Tissues from Brain CT Images using BAM type Artificial Neural Network

Abstract

Soft tissues segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. A computer software system is designed for the automatic segmentation  of brain CT images. Image analysis methods were applied to the images of 30 normal and 25 benign,25 malignant images. Textural features extracted from the gray level co-occurrence matrix of the brain CT images and bidirectional associative memory were employed for the design of the system. Best classification accuracy was achieved by four textural features and BAM type ANN classifier. The proposed system provides new textural information and segmenting normal and benign, malignant tumor images, especially in small tumor regions of CT images efficiently and accurately with lesser computational time.

Keywords: Bidirectional Associative Memory classifier(BAM), Computed Tomography (CT), Gray Level Co-occurrence Matrix (GLCM), Artificial Neural Network (ANN).