BEYİN TÜMÖRLERİNİN BİÇİMSEL YAPILANDIRMA KULLANILARAK BİLGİSAYAR DESTEKLİ TESPİTİ

Year 2016, Volume: 21 Issue: 2, 257 - 268, 28.11.2016

Buket Doğan , Seda Kazdal Çalık Önder Demir

https://doi.org/10.17482/uumfd.270102

Cited By: 3

Abstract

Bilgisayar destekli
tespit (BDT) sistemleri görüntü işleme ve örüntü tanıma tekniklerini kullanarak
medikal görüntülerdeki normal olmayan yapıların tespit işlemine yardımcı
olmaktadır. BDT sistemleri karar verme sürecini hızlandırırken bu süreçteki
insan hatası olasılığını da azaltarak fayda sağlamaktadır. Bu çalışmada beyin
MR görüntülerinde tespit edilen ilgi alanlarını biçimsel öznitelikler
kullanılarak yeniden yapılandırılması ve sınıflandırılmasını yapabilen bir BDT
sistemi geliştirilmiştir. Geliştirilen sistem önişleme, bölütleme, ilgi alanı
belirleme ve tümör tespiti olmak üzere dört aşamadan oluşmaktadır. Geliştirilen
sistem 10 hastaya ait 497 kesit görüntüsünden oluşan REMBRANDT veri setiyle
değerlendirilmiştir. Sınıflandırma işleminde sistemin performansı karar
ağaçları ile %93,36, yapay sinir ağları ile %94,89, K-en yakın komşu ile
algoritması ile %96,93 ve Meta-Learner algoritması ile %96,93 doğruluk
oranlarına erişmiştir. Bu sonuçlar önerilen yöntemin MR görüntülerinden beyin
tümörü tespitinde etkin olduğunu ve sınıflandırma işleminin performansını
arttırdığını göstermektedir.  Kullanılan
biçimsel yapılandırma yöntemi diğer BDT uygulamalarına uyarlanabilecek şekilde
geliştirilmiştir.

 

Keywords

Biyomedikal görüntü işleme, görüntü sınıflandırma, biçimsel yapılandırma, tümör tespiti, bilgisayar destekli tespit

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Abstract

Computer aided detection (CAD) systems helps the
detection of abnormalities in medical images using advanced image processing
and pattern recognition techniques. CAD has advantages in accelerating
decision-making and reducing the human error in detection process. In this
study, a CAD system is developed which is based on morphological reconstruction
and classification methods with the use of morphological features of the
regions of interest to detect brain tumors from brain magnetic resonance (MR)
images. The CAD system consists of four stages: the preprocessing, the
segmentation, region of interest specification and tumor detection stages. The
system is evaluated on REMBRANDT dataset with 497 MR image slices of 10
patients. In the classification stage the performance of CAD has achieved
accuracy of 93.36% with Decision Tree Algorithm, 94.89% with Artificial Neural
Network (Multilayer Perceptron), 96.93% with K-Nearest Neighbour Algorithm and
96.93% with  Meta-Learner (Decorate)
Algorithm. These results show that the proposed technique is effective and
promising for detecting tumors in brain MR images and enhances the
classification process to be more accurate. The using morphological
reconstruction method is useful and adaptive than the methods used in other CAD
applications.

Keywords

Biomedical image processing, Image classification, Morphological reconstruction, Tumor Detection, computer aided detection

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