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Register a new userA comparison of Image Enhancement Techniques for Recognizing and Classifying Automatically the Medical Images and implement on MRI brain Image
مقارنة بين تقنيات تحسين الصور للتعرف على الصور الطبية تلقائيًا و تصنيفها و تنفيذها على صورة دماغ التصوير بالرنين المغناطيسي
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إن الحجم الهائل للصور الرقمية المنتجة من المشافي تزداد بسرعة. الصور الطبية يمكن أن تلعب دوراً مهماً بالمساعدة في التشخيص و المعالجة. و يمكن أن تكون مفيدة أيضاً في مجال التعليم لطلاب الطب بواسطة الشرح لهذه الصور الذي يساعدهم في دراستهم. مجال جديد لاستعادة الصور باستخدام تصنيف الصور الالي تمت مناقشته خلال السنوات الماضية. تصنيف الصور الطبية يمكن أن يلعب دوراً مهماً لأغراض التشخيص و التدريس الطبية. لهذه الاسباب عدة معالجات للصور تم استخدامها. في هذه الورقة أولاً: تمت دراسة مجموعة من الطرائق المتضمنة خلال خطوات معالجة الصور الطبية, مثل المرشح الوسيط, و معادلة الرسم البياني. ثانياً: تحديد و استخراج الخصائص الهامة للصور, كمصفوفة التدرج الرمادي. ثالثاً: تقنيات التصنيف و التي تقسم الى ثلاث طرق: 1- تصنيف الاكساء, 2- تصنيف الشبكات العصبونية, 3- تصنيف ك- أقرب جار. رابعاً: تم في هذا البحث استخدام صور الرنين المغناطيسي للدماغ لتحديد منطقة الورم في الدماغ. تبدأ الخطوات بإجراء معالجة أولية للصورة قبل إدخالها الى الخوارزمية بتحويلها إلى صورة ثنائية بتدرج رمادي ليتم بعد ذلك إزالة المعلومات النصية من الصورة (معلومات المريض و بارامترات صورة الدماغ) و ذلك باستخدام خوارزمية خاصة، بعد ذلك يتم إزالة أجزاء الجمجمة من صورة الدماغ دون التأثير على المادة البيضاء و المادة الرمادية في الدماغ. ثم بعد ذلك يتم استخدام مرشح معدل (مطور) عن المرشح الوسيط لإزالة الشوائب من الصورة الرقمية الناتجة.
The amount of digital images that are produced in hospitals is increasing rapidly. Effective medical images can play an important role in aiding in diagnosis and treatment, they can also be useful in the education domain for healthcare students by explaining with these images will help them in their studies, new trends for image retrieval using automatic image classification has been investigated for the past few years. Medical image Classification can play an important role in diagnostic and teaching purposes in medicine. For these purposes different imaging modalities are used. There are many classifications created for medical images using both grey-scale and color medical images. In this paper, different algorithms in every step involved in medical image processing have been studied. One way is the algorithms of preprocessing step such as Median filter [1], Histogram equalization (HE) [2], Dynamic histogram equalization (DHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE). Second way is the Feature Selection and Extraction step [3,4], such as Gray Level Co-occurrence Matrix(GLCM). Third way is the classification techniques step, which is divided into three ways in this paper, first one is texture classification techniques, second one is neural network classification techniques, and the third one is K-Nearest Neighbor classification techniques. In this paper, we have use MRI brain image to determine the area of tumor in brain. The steps started by preprocessing operation to the image before inputting it to algorithm. The image was converted to gray scale, later on remove film artifact using special algorithm, and then remove the Skull portions from the image without effect on white and gray matter of the brain using another algorithm, After that the image enhanced using optimized median filter algorithm and remove Impurities that produced from first and second steps.
References used
Kesari Vermaa, Bikesh Kumar Singhb, A.S. Thokec, (ICCC 2015)- " An Enhancement in Adaptive Median Filter for Edge Preservation", nternational Conference on Computer, Communication and Convergence
Miss. Sukanya V. Aher1, Mrs. S. S. Vasekar2, April 2016- " A Review: Histogram Equalization Algorithms for Image Enhancement using FPGA", International Journal of Advanced Research in Computer and Communication Engineering Vol. 5,Issue
E. L. Hall, Kruger RP, Dwyer SJ, Hall DL, Mclaren RW, Lodwick GS, 1971- ” A survey of preprocessing and feature extraction techniques for radiographic images. IEEE Transactions on Computers;20:1032–44
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