Journal of Information Engineering and Applications www.iiste.org ISSN 2224-5758 (print) ISSN 2224-896X (online) Vol 1, No.4, 2011 34 | Page www.iiste.org Texture Feature Based Analysis of Segmenting Soft Tissues from Brain CT Images using BAM type Artificial Neural Network A.PADMA 1* , R.SUKANESH 2 1. Research Scholar, Thiyagarajar College of Engineering , Madurai – 625 015, India . 2. Professor of ECE, Thiagarajar College of Engineering, , Madurai – 625 015, India. * Email: giri_padma2000@yahoo.com 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). 1.INTRODUCTION In recent years, medical CT Images have been applied in clinical diagnosis widely. That can assist physicians to detect and locate pathological changes with more accuracy. CT images can be distinguished for different tissues according to their different gray levels. The images, if processed appropriately can offer a wealth of information which is significant to assist doctors in medical diagnosis. A lot of research efforts have been directed towards the field of medical image analysis with the aim to assist in diagnosis and clinical studies (Duncan, et al 2000). Pathologies are clearly identified using automated computer aided diagnostic system (Tourassi et al 1999). It also helps the radiologist in analyzing the digital images to bring out the possible outcomes of the diseases. The medical images are obtained from different imaging systems such as MRI scan, CT scan and Ultra sound B scan. The CT has been found to be the most reliable method for early detection of tumors because this modality is the mostly used in radio therapy planning for two