Segmentation and classification of MRI to detected brain tumors used FCM and SVM.

dc.contributor.authorBOUCENNA, Radhia
dc.contributor.authorMEHEMEL, Imene
dc.date.accessioned2022-01-05T07:59:06Z
dc.date.available2022-01-05T07:59:06Z
dc.date.issued2020
dc.description.abstract􀀀w 􀀀 ¢ ¤ ­ryW 􀀀 |􀀀r ¯􀀀 rbt`§ ¤ A d 􀀀 ¨ ­ÐAK 􀀀 A§® l wm ¤􀀀 Tlt w¡ A d 􀀀 C¤ T yt ºAW ¯ ¨syVAn m 􀀀 y r A r§wOt 􀀀 Am`tFA Anm An¡¤ . yb r ¯􀀀¤ dym AhS` ­ d`t Ty EC􀀀w Ah ¤􀀀 Ay EC􀀀w T A`tF¯􀀀 § £d ¤ 􀀀@¡ ¨fk§ ¯ , Cw 􀀀 Kk 􀀀 ¨ T R􀀀¤ ­º􀀀r ¨¶AO ¯􀀀 ysn 􀀀 􀀀zy 􀀀r tF¯ 􀀀@¡ ¤ GLCM ybW ­CwO 􀀀 T¶z t FCM ¹ T¶z t 􀀀 􀀀ry 􀀀¤  AOq ¤􀀀 T AR􀀀  ¤ £d§d ¤ Cw 􀀀 ¨ 􀀀 w}wl At ¯􀀀 T ys ¤ Ty A 􀀀 T Cd 􀀀 . ¢ w d§d T ¤¥sm 􀀀 SVM ynOt 􀀀 Ty EC􀀀w , SVM , FCM , C¤ ,¨syVAn m 􀀀 y r A r§wOt 􀀀 , ynOt 􀀀 ,T¶z t 􀀀 : Ty Atfm 􀀀 Amlk 􀀀 .GLCM R´esum´e Une tumeur c´er´ebrale est une masse ou une croissance de cellules anormales dans le cerveau. Elle est consid´er´ee comme une maladie grave et se pr´esente sous plusieurs formes, certaines b´enignes et d’autres malignes. Ici, nous avons utilis´e l’imagerie par r´esonance magn´etique (IRM) pour donner un r´esultat de lecture clair dans la d´etection de la tumeur, cela seul ne suffit pas, mais nous devons utiliser des algorithmes, dont le premier est l’algorithme de segmentation FCM pour segmenter l’image puis appliquer GLCM pour extraire les caract´eristiques du tissu statistique de second ordre et am´eliorer la pr´ecision de la production pour atteindre et identifier la tumeur sans ajouter ni diminuer et enfin l’algorithme de classification SVM charg´e de d´eterminer son type. Mots-cl´es : Segmontation, Classification, IRM, Tumeur, FCM, SVM, GLCM. Abstract A brain tumor is a mass or growth of abnormal cells in the brain. It is considered a serious illness and comes in many forms, some benign and others malignant. Here we have used magnetic resonance imaging (MRI) to give a clear reading result in tumor detection, this alone is not enough, but we have to use algorithms, the first of which is FCM segmentation algorithm to segment image then apply GLCM to extract second order statistical tissue characteristics and improve production precision to reach and identify tumor without adding or shrinking and finally SVM classification algorithm to determine its type. Key-words: Segmontation, Classification, MRI, Tumor, FCM, SVM, GLCM.en_US
dc.identifier.issnMM/657
dc.identifier.urihttp://10.10.1.6:4000/handle/123456789/1656
dc.language.isoenen_US
dc.publisherUniversité Mohamed el-Bachir el-Ibrahimi Bordj Bou Arréridj Faculté de Mathématique et Informatiqueen_US
dc.subjectKey-words: Segmontation, Classification, MRI, Tumor, FCM, SVM, GLCM.en_US
dc.titleSegmentation and classification of MRI to detected brain tumors used FCM and SVM.en_US
dc.typeThesisen_US

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Imagery via magnetic resonance is a type of imaging that allows you to see detailed pictures of your organs and tissues within your body. The doctors use it to get a sense of what’s going on inside the body and to pinpoint the problem, but this isn’t enough because the human body is a complex structure, particularly the human brain, which is frequently affected by a tumor, which can be malignant or benign and determining the latter is difficult. The radiology department needed an effective segmentation method that did not rely on a manual or semi-automated solution with inexact results and a protracted implementation time, as well as a categorization methodology that determines his kind. Due to the significance of obtaining precise results, a bibliographic review of picture segmentation and classification methods was conducted, allowing us to better understand the variety of methods for segmenting cerebral tissue and classifying its kind. In the literature, several segmentation methods have been proposed, including subsistence segmentation and region segmentation. In terms of the categorization approach, it has been divided into two categories: controlled and uncontrolled. Our work’s goals include obtaining a specific result in a short period, so we followed the steps below: first, filtering an IRM brain image, then segmenting the image that was filtered by the FCM algorithm, which gave us a segmented image that could be used to identify each part of the brain and finally, applying the tagging algorithm. Produced using segmentation to extract the characteristics of a second-order statistical texture based on the density points in an image and fourth, by employing the SVM classification method, which allows us to classify images based on data and determine the outcome. We presented the results of our research in order to use the best segmentation and classification algorithms for medical pictures in order to improve the quality of brain 55 4 RESULTS AND EVALUATION tumor detection. All of the steps were carried out on a bidimensional IRM image of the brain, and the result is a collection of pathological data analyses that provide a realistic picture to the clinician.

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