Classification of Normal Brain Tissues in Computed Tomography Radiographs Using Image Texture Analysis

الموضوعات

طب

الملخص

This study was conducted to classify normal brain tissue in computed tomography (CT) radiographs using image texture analysis; into white matter, grey matter CSF and bone using texture feature extracted from CT images. The extracted feature classified using linear discriminate analysis. The data obtained from Modern Medical Center [Sudan/ Khartoum], and King Khalid Hospital [KSA/ Kharj], the study samples were consisted of 140 patients’ images with normal brain underwent CT brain examination. The data was collected in period from July 2017 October 2017. The images were analyzed using Interactive Data Language IDL software. The results of this study showed that the overall accuracy of classification was 97.0%; with the accuracy of classification for Bone was 99.5%, White matter was 97.3%, Gray matter was 95.3%, and CSF was 96.2%. Study showed texture analysis using first order statistics can be used to classify normal brain tissues with high degree of accuracy; therefore abnormal tissues can be identified with an acceptable accuracy.

روابط وملفات

التعريف والنوع

رقم الوثيقة

ebfcd5a6-5d00-4b80-b337-5f2557ba869e

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

17986_1.pdf

بيانات النشر

ترجمة العنوان

تصنيف أنسجة الدماغ الطبيعية في صور الأشعة المقطعية المحوسبة بإستخدام تحليل نسيج الصورة

ألقاب المؤلفين

[{"name_ar":" Soultana Awad Mohamed Ahmad","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Mohamed Elfadil M. Gar-enabi","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Classification of Normal Brain Tissues in Computed Tomography Radiographs Using Image Texture Analysis

المحتوى والصفحات

عدد الصفحات

0

إشراف وإعداد

الإشراف

Mohamed Elfadil M. Gar-enabi

الإعداد

Soultana Awad Mohamed Ahmad

الاقتباسات الببليوغرافية