bec-computer science-prakash h unki

7/31/2019 BEC-Computer Science-Prakash H Unki

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A Ph.D. Synopsis on

Recognition, Analysis and Interpretation of

Certain Brain MRI Images

Submitted by: Prakash H. UnkiAssistant Professor

Department of Computer ScienceB.L.D.E.As Dr. P.G.H College of Engg. and Tech.,Bijapur

Under the Guidance : Dr. Basavaraj S. AnamiProfessor and Head

Department of Computer ScienceBasaveshwar Engineering College, Bagalkot

Research Centre

Department of Computer ScienceBasaveshwar Engineering College, Bagalkot

Visvesvaraya Technological University (VTU)

Belgaum, Karnataka

Recognition, Analysis and Interpretation of Certain Brain MRI

Images


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Digital Image Processing (DIP) refers to processing of digitized images throughcomputers and finds varieties of applications in the most diverse areas of business,science and technology. Any typical image processing application comprises ofextraction of important features of a given image, using which later description,interpretation, or understanding of an image is provided. DIP techniques are used todayin solving varieties of problems related to medical, office and industrial automation,remote sensing, science, criminology, image transmission and storage, astronomy,space, meteorology, information technology, entertainment, consumer electronics,printing, graphic arts and defense. Digital images are captured through imaging devices

and cover almost the entire frequency spectrum, ranging from gamma to radio waves.The images generated by these devices include ultrasound, MRI, electron microscopyetc. to which humans, many a times, are not accustomed to. Medical applications ofDIP include: ECG, EEG, EMG analysis; cytological, histological and stereologicalapplications; automated radiology and pathology; X-ray image analysis; massscreening of medical images such as chromosome slides for detection of variousdiseases, mammograms, cancer smears; CAT, MRI, PET, SPECT, USG, and othertomographic images; routine screening of plant samples; 3-D reconstruction andanalysis, et cetera.

In the recent years, the most important diagnostic tool in medical applications ismedical imaging. This is a group of non-invasive techniques, pioneered by WilhelmRoentgen, for visual probing of the human body. The impressive range of sophisticatedand versatile medical imaging devices, with popular acronyms such asCT/DXA/MRI/PET/SPECT/USG, accentuates the need for a shift from manuallyassessed images towards efficient, accurate and reproducible computer-based methods.These methods aim at assisting medical experts in their decisions by providing themwith quantitative measures inferred from the above-mentioned imaging modalities.These are the most helpful modalities in the study of brain and its related diseases.

The brain is the most fascinating and least understood organ in the human body. Forcenturies, scientists and philosophers have pondered the relationship between behavior,emotion, memory, thought, consciousness, and the physical body. The study of thehuman brain has entered a new era, offering new insights into neurology, psychiatry,psychology and perhaps even contributing to the philosophical debate about therelationship between mind and brain. There are various types of brain diseases like


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Cerebrovascular Diseases- (stroke or brain attack) which includes Multiple embolicinfarction, Acute stroke, Subacute stroke, Chronic subdural hematoma, Cavernousangioma, Arteriovenous malformation, Vascular dementia, Hypertensiveencephalopathy, Fatal stroke, Cerebral hemorrhage, Neoplastic Disease- (brain tumor)which includes Glioma, Metastatic bronchogenic carcinoma, Meningioma, Sarcoma,Degenerative Disease which includes Mild Alzheimer's disease, Alzheimer's disease,Huntington's disease, Motor neuron disease, Cerebral calcinosis, Pick's disease,Inflammatory or Infectious Disease which includes Multiple sclerosis. There areseveral types of non-invasive brain imaging techniques such as electroencephalography(EEG), magneto encephalography (MEG), anatomical magnetic resonance imaging(MRI), functional MRI (fMRI), positron emission tomography (PET) and others.

Magnetic resonance imaging (MRI) is a very versatile imaging modality which is usedto acquire several different types of images. Some examples include anatomicalimages, images showing local brain activation and images depicting different types ofpathologies. It is based on the phenomenon of nuclear magnetic resonance (NMR). Itproduces images of the human body with excellent soft tissue contrast, allowing

neurologists to distinguish between grey and white matter, study brain function andvarious brain defects such as tumors, stroke etc. Since MRI involves no ionizingradiation, the subjects are at minimized risks. Radio waves are used in MRI andproduce pictures in any plane. This technique places a patient in a powerful magneticmedium, and passes radio waves through the body in short pulses. Each pulse causes aresponding pulse of radio waves to be emitted by the patients tissues. The locationfrom which these signal originate and their strengths are determined by a computer,which generates a two-dimensional picture of a section of the patients body.

The literature survey carried out related to technology impact in the study of brain

related diseases revealed that a fair amount of research has gone into this area. Analysisand diagnosis of various brain related diseases like brain stroke using neuralnetwork[1], atherosclerotic disease in human carotid arteries[2], basal ganglia foraccurate detection of human spongiform encephalopathy[3], brain tumors[4],Alzheimers disease(AD)[5], brain infarct, infection, hamartoma, and tumor[6],neurosarcoidosis(NS)[7], cystic or necrotic brain tumors[8], different pathologicsituations[9], nodular enhancement of the oculomotor nerve [10],Hemangioblastoma(Tumors) of the conus medullaris[11], MS lesions[12][13],Parkinsons disease[14], pathological/normal brain[15], HIV/AIDS[16] are being cited

in literature on processing of Brain MRI images.

Brain MRI segmentation (for different applications) by applying different techniquessuch as nonparametric density estimation[17], Topology-preserving, anatomy-drivensegmentation (TOADS)[18], atlas-based whole brain segmentation method with anintensity renormalization procedure[19], a knowledge-driven algorithm[20],tractography techniques[21], fuzzy logic[22][23][24], self-organizing map(SOM)


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neural network[25], k-means objective function combined genetic algorithm[26],Hidden Markov Model (HMM) [27], analysis of brain MRI data using registration-based on deformation tensor morphometry [28], learning-based method[29], activemarkers[30] are being cited in the literature.

We have come across works like detection of brain activation using conditionalrandom field (CRF)[31], age-related changes brain white matter(WM)[32], analyzingregions of neuronal activation[33], brain development and fetal brain pathology[34],effect of caffeine on verbal working memory task[35], neural correlates of retrievalsuccess for music memory[36] and early functional brain development with datacollected from children during natural sleep[37].

Extraction of texture properties of the brains white matter (WM)[38], sphericalwavelet transformation to extract shape features of cortical surfaces[39],single celldetection[40], Bayesian decision theory applied to brain tissue classification[41],computation and visualization of volumetric white matter connectivity in diffusiontensor(DT) MRI[42], topological visualization of human brain diffusion MRI[43],

labeling of structures in 3D brain MRI data sets using expert anatomical knowledgethat is coded in fuzzy sets and fuzzy rules[44] are found in literature.

This being the state-of-the-art in the area of brain Magnetic Resonance Imaging used toidentify diseases, effect of external stimulus on brain functions, segmentation ofspecific section in brain to study brain activities and registration of brain images, weare motivated to investigate the MRI imaging in interpretation, analysis of braindiseases commonly found in North Karnataka and South Maharasthra regions. In theseregions, commonly found brain diseases are tuberculoma, neurocysticercosis,infarction, hematoma, tumor and multiple sclerosis. Statistics shows that everyday one

or two patients are diagnosed with these diseases. Not much work is cited in theliterature, to the best of our knowledge, connected to these diseases. Scope exists foridentification, interpretation and analysis of these important brain diseases. Eventhough some work is done for identification of tumors, but classification andquantification of tumors based on size, location, orientation etc. is considered to be adifficult and challenging task.

The study of these brain disorders requires accurate tissue segmentation from MRIimages of the brain. Manual delineation of the three brain tissues, white matter(WM),

gray matter (GM), and cerebrospinal fluid(CSF), in MRI images by a human expert istoo time-consuming for studies involving larger databases. Here, the doctors can availthe technology support. In addition, the lack of clearly defined edges induces largeintra and inter observer variability, which deteriorates the significance of the analysisof the resulting segmentation, thus calling for efficient automatic segmentationmethods. Manual interpretation of these diseases is also less accurate, varies fromdoctor to doctor and their expertise. Looking at all these facts, it is evident that there is


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a dire need for investigation for technological support in this area and hence followingare the broad objectives of the proposed work.

The methodology being devised would certainly assist medical experts in theirdecisions in terms of quantitative measures resulting in automated analysis andinterpretation of brain MRI images. Interpretation pertains not only partitioning of animage into object and background, but also to provide descriptions of functionalproperties and relations inferred from MRI images. Providing such high-leveldescriptions, also called image understanding, would help the doctors in theirdiagnosis. Constructing a complete system that infers functional indices from brainMRI images without manual interaction and preferably within a reasonable timeframeis also an objective of this work. Quantitative analysis of the developed methodswould be taken up. Surprisingly, this seems not always to be the case in the medicalimage analysis literature. Existing methods present qualitative results in a few subjectsand completely lack quantitative validation, which are of very little use to the clinicalpractitioners. This would remain the ultimate goal of the proposed work.

Furthermore, the work involves collection of brain MRI images from differenthospitals, radiology centers, both normal and abnormal. A web database for researcherswill be created from the images so collected, as such databases are a few in numbers atpresent. The tissue segmentation process is carried out to separate WM, GM, and CSFusing different techniques. The different shape features(location, area, orientation etc.),texture features (GLRM(Grey level Run Length Matrix), GLCM(Grey levelCooccurrence Matrix), texture anisotropy, laminarity etc.) and histogram features willbe extracted and a database is going to be created. These features will be used tointerpret, analyze, classify and recognize the above mentioned brain diseases. It willhelp the radiologists, physicians and surgeons in their decision process by providing

accurate data within reasonable timeframe. The work can be extended to analyze theother brain diseases.

The work on medical image analysis is typically highly interdisciplinary. It will drawon results from multivariate statistics, numerical analysis, linear algebra, wavelettheory, medicine, MR physics, computational geometry, computer science, computergraphics, etc. In this work, the technologies like Artificial Neural Network(ANN),Fuzzy Logic, Genetic Algorithm, Hidden Markov Model(HMM), Support VectorMachine(SVM), Wavelets, Level sets will be explored to interpret, analyze, and

recognize the various brain diseases.


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To summarize, the proposed work aims at recognition, interpretation, and analysis ofMRI brain images concerned with diseases such as tuberculoma, neurocysticercosis,infarction, hematoma, tumor and multiple sclerosis. It aims to provide fast, automaticmethods and to introduce more robustness and regularity for interpretation of thesediseases.

The authors wish to thank Dr. Prashant B. Katakol, Neurosurgeon, SanjiviniDiagnostics, Bijapur, Dr. Ramesh V. Manakare, Radiologist, Dr. Bhagavati,Neurophysician, BLDEAs B.M. Patil medical college and research centre, Bijapur,Dr. M.S. Kotennavar, Al-ameen medical college, Bijapur, Dr. Ashutosh Pavale,Radiologist, Keludi hospital and research centre, Bagalkot and Dr. Avinash Vernekar,Radiologist, Sushruth CT and MRI Centre, Solapur for their help and valuablesuggestions in formulating the problem.

Signature of the Candidate Signature of the Guide

Prakash H. Unki, Dr. Basavaraj S. Anami,Asst. Prof., Dept. of CSE, Prof. & Head, Dept. of CSE,BLDEAs Dr. P.G.H College of Engg.and Tech., Basaveshwar EngineeringCollegeBijapur-586103 BAGALKOT 587 102

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