Computer aided brain tumor detection with histogram equalization and morphological image processing techniques

Ülkü, Eyüp Emre; Camurcu, Ali Yilmaz

doi:10.1109/icecco.2013.6718225

Cited by 22 publications

(11 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work realized by [12] describes a computer-aided detection system for detecting tumors. This framework is based on histogram equalization and morphological mathematical operations.…”

Section: Related Workmentioning

confidence: 99%

Automatic brain tumor tissue detection based on hierarchical centroid shape descriptor in Tl-weighted MR images

Ilunga-Mbuyamba

Aviña-Cervantes

Lindner

et al. 2016

2016 International Conference on Electronics, Communications and Computers (CONIELECOMP)

View full text Add to dashboard Cite

The brain tumor tissue detection allows to localize a mass of abnormal cells in a slice of Magnetic Resonance (MR). The automatization of this process is useful for post processing of the extracted region of interest like the tumor segmentation. In order to detect this abnormal growth of tissue in an image, this paper presents a novel scheme which uses a two-step procedure; the k-means method and the Hierarchical Centroid Shape Descriptor (HCSD). The clustering stage is applied to discriminate structures based on pixel intensity while the HCSD allow to select only those having a specific shape. A bounding box is then automatically placed to delineate the region in which the tumor was found. Compared to the tumor delineation performed by an expert, a similarity measure of 91% was reached by using the Dice coefficient. The tests were carried out on 254 T1-weighted MRI images of 14 patients with brain tumors.

show abstract

“…The work realized by [12] describes a computer-aided detection system for detecting tumors. This framework is based on histogram equalization and morphological mathematical operations.…”

Section: Related Workmentioning

confidence: 99%

Automatic brain tumor tissue detection based on hierarchical centroid shape descriptor in Tl-weighted MR images

Ilunga-Mbuyamba

Aviña-Cervantes

Lindner

et al. 2016

2016 International Conference on Electronics, Communications and Computers (CONIELECOMP)

View full text Add to dashboard Cite

show abstract

“…A neuro‐fuzzy segmentation procedure of the MRI information is introduced in to distinguish different tissues like white matter, gray matter, CSF, and tumor. Some other critical investigates are in …”

Section: Introductionmentioning

confidence: 99%

“…Some other critical investigates are in. [19][20][21] The combination of multiple classifiers that are utilized from that K-Nearest Neighbor (KNN) algorithm effectively classified the brain tissues. Moreover, MRF (Markov Random Field) method powerfully employed, but this method has a high computational cost.…”

mentioning

confidence: 99%

Identification and classification of brain tumor through mixture model based on magnetic resonance imaging segmentation and artificial neural network

Deepa¹,

Emmanuel

2016

Concepts Magnetic Resonance

View full text Add to dashboard Cite

Detection of brain tumor in Magnetic Resonance Imaging (MRI) is vital as it delivers data about unusual tissues which is essential for planning treatment. Automating this method is challenging due to the high variety in appearance of tumor tissue among dissimilar patients and in many circumstances, comparison between tumor and normal tissue. In this article, we presented a mixture model based segmentation and classification of brain MRI for tumor identification. The proposed robust mixture estimator combining trimming of the outliers is based on component wise confidence level ordering of observations. The proposed method consists of three stages. In the first stage, the brain MRI is segmented into white matter (WM), gray matter (GM), Cerebrospinal fluid (CSF), and outliers by ordering of observations. In second stage, outliers consists of tumor cells in which eight type of features Contrast, Correlation, Homogeneity, Energy, Entropy, Standard deviation, Skewness, and Kurtosis are extracted. In the third stage, the extracted features are trained by Artificial Neural Network (ANN)and based on this a brain tumor identification scheme is established to examine those features to judge whether brain tumor is present in the given image or not. Experimental results indicate that the proposed classification method has achieved 93.47% in sensitivity, 100% in specificity, and 96.34% accuracy with less computational time based on the number of extracted features when compared to earlier classification methods.

show abstract

“…Methods developed for this purpose include artificial neural networks (Logeswari and Karnan, 2010), masking (Akram and Usman, 2011), grey scale thresholding (Rulaningtyas and Ain, 2009), fuzzy logic (Khotanlou, Colliot, Atif, and Bloch, 2009), rule-based approaches (Chan, 2007) and clustering methods (Wu, Lin, and Chang, 2007). Furthermore, using different classification methods after identifying a potential tumor region is another commonly used method of detection (El-Dahshan, Hosny, and Salem, 2010; Jayachandran and Dhanasekaran, 2013; Naik and Patel, 2014; Ulku and Camurcu, 2013). In such methods, other factors like structural quantities also become applicable since features derived from pixel values are inadequate in most cases (Xuan and Liao, 2007).…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Doğan¹,

Calik²,

Demir³

2016

Uludağ University Journal of the Faculty of Engineering

View full text Add to dashboard Cite

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

show abstract

Computer aided brain tumor detection with histogram equalization and morphological image processing techniques

Cited by 22 publications

References 4 publications

Automatic brain tumor tissue detection based on hierarchical centroid shape descriptor in Tl-weighted MR images

Automatic brain tumor tissue detection based on hierarchical centroid shape descriptor in Tl-weighted MR images

Identification and classification of brain tumor through mixture model based on magnetic resonance imaging segmentation and artificial neural network

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Contact Info

Product

Resources

About