Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques

Gopal, Nanda; Karnan, M.

doi:10.1109/iccic.2010.5705890

Cited by 109 publications

(30 citation statements)

References 6 publications

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“…The purpose of specifying the regions of interest is to reveal potential abnormal structures and conduct search on these areas in order to obtain results as fast as possible (Ambrosini, Wang, and O'Dell, 2010; Zarandi, Zarinbal, Zarinbal, Turksen, and Izadi, 2010). Rule-based approaches, template matching, nearest-neighbor clustering, artificial neural networks and Bayes classifier are few examples for the methods used in classification of regions of interest and detection of tumors (Gopal and Karnan, 2010). …”

Section: Computer-aided Detection Systemmentioning

confidence: 99%

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Doğan¹,

Calik²,

Demir³

2016

Uludağ University Journal of the Faculty of Engineering

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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. Beyin Tümörlerinin Biçimsel Yapılandırma Kullanılarak Bilgisayar Destekli TespitiÖz: 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.Anahtar Kelimeler: Biyomedikal görüntü işleme, görüntü sınıflandırma, biçimsel yapılandırma, tümör tespiti, bilgisayar destekli tespit

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Section: Computer-aided Detection Systemmentioning

confidence: 99%

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Doğan¹,

Calik²,

Demir³

2016

Uludağ University Journal of the Faculty of Engineering

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“…Gopal,N.N. Karnan, M. [5] suggested an algorithm which used multi-scale image segmentation. M E Jain explained a wrapper based technique for image segmentation [6].…”

Section: Related Workmentioning

confidence: 99%

Application of Edge Detection for Brain Tumor Detection

Sharma¹,

Diwakar²,

Choudhary³

2012

IJCA

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Brain tumors are created by abnormal and uncontrolled cell division in brain itself. If the growth becomes more than 50%, then the patient is not able to recover. So the detection of brain tumor needs to be fast and accurate. The objective of this paper is to provide an efficient algorithm for detecting the edges of brain tumor. The first step starts with the acquisition of MRI scan of brain and then digital imaging techniques are applied for getting the exact location and size of tumor. MRI images consist of gray and white matter and the region containing tumor has more intensity. So, first noise filters are used for noise removal and then enhancement techniques are applied to the given MRI scan of brain. After that the basic morphological operations are applied for extracting the region suffering from tumor. And then verification of region detected is done by using watershed segmentation. General TermMorphological Segmentation

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“…Gopal,N.N. Karnan, M. [4] suggested an algorithm which used multi-scale image segmentation, this algorithm was based on fuzzy c-mean algorithm for the detection of brain tumor. Joshi, D.M.…”

Section: Analysis and Findingsmentioning

confidence: 99%

An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation

Mustaqeem¹,

Ali²,

Fatima³

2012

IJIGSP

185

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Abstract-During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this paper for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image.

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Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques

Cited by 109 publications

References 6 publications

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Application of Edge Detection for Brain Tumor Detection

An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation

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