INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 01, JANUARY 2020 ISSN 2277-8616 2519 IJSTR©2020 www.ijstr.org High Level Binary Feature Vector For Classification Of T1c+ Ring Enhancing Lesions R. Anita Jasmine, P. Arockia Jansi Rani, Evangeline Ebenezer Abstract: Human observations with limitations, quests for advance in techniques to assist automated diagnosis and further treatment. Predictive analytics encompasses several statistical data processing methods to interpret current data. This is based on the past facts serving as a in dispensable tool for radiology. Image processing tasks like segmentation, feature extraction, classification, and retrieval are used for computer aided diagnosis and treatment. The aim of this work is to analyze the soft tissue pattern in Ring Enhancing Lesion (REL) in MRI which is crucial for prediction whether the lesion is benign or malignant. A novel method to compute a High Level Binary Feature Vector (HLBFV) for heterogeneous, partial and homogeneous soft tissue classification is introduced to address the ambiguity of numerical feature values in pattern recognition. HLBFV is binary, comprehensible and supports very quick image retrieval. It is termed as high level feature as it is constructed from low level statistical features computed from laws filter and GLCM features. After automatic segmentation, REL tissue is enhanced using s elected Law’s masks for better discrimination of soft tissue pattern .The image is divided into non overlapping equal blocks for extracting several low level features to capture the intensity distribution, variation and correlation throughout the core of the lesion. The computed texture values are normalized to make it rotation invariant. From the low level values, the high level feature vector is constructed using the classification rules. Mean variance of intensity on different image blocks and experimentally verified threshold values on the dataset is used for classification. The performance of the classification is evaluated. The method achieves 100% accuracy for heterogeneous, 99% for partial and 90% for homogeneous lesions. Index Terms: Feature vector, GLCM features, High Level Feature vector, Law’s filter, MRI, Ring Enhancing Lesion, Classification,T1C+. ——————————  —————————— 1 INTRODUCTION Aring Enhancing Lesion (REL) is an abnormal radiologic sign obtained using radio contrast medium to improve the visualization of anatomical internal structure. It is the most common radiological abnormality seen in young Indian patients with epilepsy [1]. REL are characterized by an area of decreased density surrounded by a bright rim from concentration of the enhancing dye. Contrast enhanced Magnetic Resonance Imaging (MRI) or CT images are used to diagnose REL. Basically there are eight different RELs. They are classified lesion pattern, completeness of the ring, smooth or irregular margins of the rim [2]. MRI images provide anatomical and physiological details in structure and function with 3D orientation, excellent soft tissues visualization and high spatial resolution [3].Over the last two decades, MRI has emerged as the ideal modality for evaluating soft tissue lesions [4]. Advances in MRI technology improve the diagnostic accuracy of tumor, surgical planning and treatment. The role of MRI in differentiating benign and malign tumor is based on signal intensity and pattern of tumor soft tissue. Usually, MRI contrast agents enhances the signal intensity of tumors like REL on T1-weighted spin-echo MR images, in some cases like FLAIR images, the demarcation between tumor and edema provides information on on tumor vascularity [5][6].The issue of distinguishing benign and malignant tumor can be successfully solved using MRI. Benign malignant differentiation in more than 90% cases can be predicted based on morphological features as suggested in [7]. The contrast enhancement pattern in tumor soft tissue is analyzed for tumor prediction. Homogeneous enhancement is uniform and confluent throughout the mass. Heterogeneous enhancement is non uniform and varies within the mass. Homogeneous contrast enhancement pattern highly specifies benignity, and heterogeneous enhancement is moderately specific for malignancy [8]. In this paper, a novel method for analyzing the soft tissue pattern which is crucial for tumor classification is presented. In MRI, the tumor is completely isolated using histogram based automatic region growing method [9]. So as accuracy is not influenced by the surrounding tissues. HLBFV constructed from low level features, is binary which subsequently speeds up the retrieval process. The images are filtered using two selected masks from Laws filter to enhance the texture features. Normalized GLCM features are computed for non overlapping equal size blocks. Classification is performed using mean variance of image intensity across different regions and experimentally verified threshold values. The rest of the paper is organized as follows. The related works are summarized in section II. A short overview about the enhancement pattern of mass is discussed in with illustration in section III. Section IV details the phases of proposed method with mathematical definitions. Section V evaluates the performance of the proposed method followed by the conclusions and discussions in section VI. 2 RELATED WORKS As radiomics is gaining attention in recent years, several image processing techniques are being proposed to improve the accuracy of computer aided diagnosis in medical imaging. Feature extraction plays the core role in image segmentation, classification and retrieval. Feature extraction techniques capture the intensity information, texture distribution and shape variation, using statistical methods, wavelet based methods and structural methods. Chung et al [10] proposed a method for less experienced radiologist to identify benign or malignant lesion classification using systematic combination of depth, size and signal intensity. They focused on assessing three different benign and three different malignant tumors. The work concludes significant difference in signal heterogeneity between benign and malignant lesions. Hauptfleisch et al [11] assessed lesions in MRI according to ———————————————— • R. Anita Jasmine is currently pursuing Ph.D degree program in computer science and engineering in Manonamiam Sundaranar University, India, PH-9176027181 E-mail: anitajasminer@gmail.com • Dr.P. Arockia Jansi Rani is currently working with the Department of Computer Science and Engineering, Manonamiam Sundaranar University, India, PH-9486666667. E-mail: jansimsuniv@gmail.com • Dr.Evangeline Ebenezer is currently working as the senior Radiologist, Jeyasaharan Memorial Hospital,India. PH-9944797202. E-mail: oesolomon@gmail.com