Development of Techniques for the Automaticextraction and Grade Detection of Gliomatumors from Conventional Brain Magneticresonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis. In the name of God, the Entirely Merciful, the Especially Merciful I would like to express my heartfelt gratitude to my supervising guide Dr. Tessamma Thomas, Professor, Department of Electronics, Cochin University of Science and Technology for her valuable guidance as well as for her kind advice, constant encouragement and affectionate support. I am greatly indebted to Dr. Bejoy Thomas, Additional Professor, Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology (SCTMIST), Kerala, India, for his valuable suggestions in the work of segmentation and grading glioma from conventional MR images and providing me important research materials. I am thankful to the anonymous reviewers of my publications for providing valuable suggestions and motivating comments. Let me express my sincere gratitude to Prof. C.K. Anandan, Head of the Department of Electronics, Cochin University of Science and Technology, for extending the facilities in the department for my research work. Also, I am grateful to Prof. K. Vasudevan, Professor and former Head of the Department of Electronics, Prof. P.R.S Pillai, Professor and former Head of the Department of Electronics,Prof. K. G. Balakrishnan, former Head of the Department of Electronics, Prof. Mohanan, Professor, Department of Electronics, Dr. James Kurian and Dr. Supiya M.H., Associate Professors, Department of Electronics, for their kind support and help. I am deeply indebted to Dr. K. P.P.Pillai, former Executive secretary, Indian Society for Technical Education (ISTE) for his affectionate support throughout my career.I thankfully remember my former Principal Dr. A.V. Zachariah (late) for his inspiring words. I gratefully acknowledge Dr. Shaji senadhipan, my former Principal and Dr. Z.A. Zoya, Principal, College of Engineering Perumon, Kollam for their encouragement for completing this work. I would like to express my sincere gratitude to Dr. Deepa P. Gopinath, Department of Electronics and Communication Engineering, College of Engineering Thiruvananthapuram and Dr.V.G. Geethamma, Department of Nanotechnology, Mahatma Gandhi University for their help and motivations provided for throughout my research. I am grateful to Prof. Bindu Prakash, Associate Professor, Department of Electrical and Electronics Engineering, College of Engineering Perumon, Kollam for her support. I thank all my fellow researchers, especially Dr.Dinesh Kumar V.P, Dr. Deepa Sankar, Dr.Praveen N., Ms. Deepa J, Ms. Reji A.P., R. Sethunath and Nobert Thomas, for their support. I thank all administrative staff and librarian of the Department of Electronics and Cochin University of Science and Technology for their cooperation and support. I am greatly obliged to my husband Suresh Bhaskar and my daughters Gayathri S and Gauthami S for their constant support and motivation to complete this thesis. It is beyond words to express my gratitude to my parents and sisters for their help and encouragement. Without their help and sacrifice, I am sure I could not have accomplished this task. With great sense of gratitude I thank Mr. Ajith A., Assistant Professor, Department of Electronics and Communication Engineering, College of Engineering Perumon, Kollam for his support and encouragement for completing the thesis