A fast and efficient computer aided diagnostic system to detect tumor from brain magnetic resonance imaging
01 Jun 2015-International Journal of Imaging Systems and Technology (John Wiley & Sons, Inc.)-Vol. 25, Iss: 2, pp 123-130
TL;DR: A simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI) that is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐mean, fuzzy c‐means, etc.
Abstract: In this work, a simple and efficient CAD computer-aided diagnostic system is proposed for tumor detection from brain magnetic resonance imaging MRI. Poor contrast MR images are preprocessed by using morphological operations and DSR dynamic stochastic resonance technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest ROI lies in the middle and its size ranges from 240 × 240 mm2 to 280 × 280 mm2. The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block-based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well-known existing techniques, like k-means, fuzzy c-means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work.
Citations
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01 Jan 2015
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230 citations
TL;DR: The proposed non-invasive CAD system based on brain Magnetic Resonance Imaging (MRIs) is capable of assisting radiologists and clinicians to detect not only the presence, but also the type of glioma tumors.
Abstract: The real time usage of Computer Aided Diagnosis (CAD) systems to detect brain tumors as proposed in the literature is yet to be explored. Gliomas are the most commonly found brain tumors in human. The proposed non-invasive CAD system based on brain Magnetic Resonance Imaging (MRIs) is capable of assisting radiologists and clinicians to detect not only the presence, but also the type of glioma tumors. The system is devised to work irrespective of the image pulse sequence. It uses different segmentation schemes for different pulse sequences, fusion of texture features, and ensemble classifier to perform three levels of classification. Once the tumor is detected at the first level of classification, its location is analyzed using tentorium of brain and it is classified into superatentorial or infratentorial in the next level. Based on the morphological and inherent characteristics of tumor (area, perimeter, solidity, and orientation), the system identifies tumor type at the third level of classification. The system reports average accuracy of 97.76% on JMCD (a dataset collected from local medical college) and 97.13% on BRATS datasets at the first level of classification. Average accuracy of 97.87% for astrocytomas, 94.24% for ependymoma, 96.29% for oligodendroglioma, and 98.69% for glioblastoma multiforme is observed for histologically classified JMCD dataset. The same is observed as 95.45% for low grade and 95.50% for high grade tumors in publically available BRATS dataset. The performance of the proposed CAD system is statistically examined through hypothetical Student’s t-test and Wilcoxon matched pair test. The performance of the system is also validated by domain experts for its possible real time usage.
57 citations
TL;DR: This work presents a non-invasive and adaptive method for detection of tumor from T2-weighted brain magnetic resonance images, enhanced by preprocessing and segmented through multilevel customization of Otsu’s thresholding technique.
Abstract: The detection of brain tumor is a challenging task for radiologists as brain is the most complicated and complex organ. This work presents a non-invasive and adaptive method for detection of tumor from T2-weighted brain magnetic resonance (MR) images. Non-homogeneous brain MR images are enhanced by preprocessing and segmented through multilevel customization of Otsu’s thresholding technique. Several textural and shape features are extracted from the segmented image and two prominent ones are selected through entropy measure. Support vector machine (SVM) classifies MR images using prominent features. Experiments are performed on a dataset collected from MP MRI & CT Scan Centre at NSCB Medical College Jabalpur and the other from Charak Diagnostic & Research Centre Jabalpur. More than 98% accuracy is reported with 100% sensitivity for both the datasets at 99% confidence interval. The proposed system is compared with several existing methods to showcase its efficacy.
43 citations
TL;DR: The proposed clinical decision support system utilizes fusion of MRI pulse sequences as each of them gives salient information for tumor identification and successfully identifies and classify tumor with Naive Bayes classifier.
Abstract: Brain tumor detection and identification of its severity is a challenging task for radiologists and clinicians. This work aims to develop a novel clinical decision support system to assist radiologists and clinicians efficiently in real-time. The proposed clinical decision support system utilizes fusion of MRI pulse sequences as each of them gives salient information for tumor identification. An adaptive thresholding is proposed for segmentation and centralized patterns are observed from LBP image of so obtained segmented image. Run length matrix extracted from these centralized patterns is used for tumor identification. The developed features successfully identify and classify tumor with Naive Bayes classifier. The proposed decision support system not only detects tumors, but also identifies its grading in terms of severity. As Glioma tumors are the most frequent among brain tumors, the proposed system is tested for the presence of low grade (Astrocytoma and Ependymoma) as well as high grade (Oligodendroglioma and Glioblastoma Multiforme) Glioma tumors on images collected from NSCB Medical College Jabalpur, India and BRATS dataset. The experiments performed on two datasets give more than 96% accuracy. The proposed decision support system is quite sensitive towards the detection and specification of tumors. All the results are verified by domain experts in real time.
40 citations
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20,442 citations
"A fast and efficient computer aided..." refers background in this paper
...Homogeneity 5 1 4 X255 i;j50 PHði; jÞ 11ji2jj 1 X255 i;j50 PVði; jÞ 11ji2jj 1 X255 i;j50 PD1ði; jÞ 11ji2jj 1 X255 i;j50 PD2ði; jÞ 11ji2jj (6) Contrast: Contrast measures the spatial frequency and amount of local variations present in the image (Haralick et al., 1973)....
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TL;DR: In this paper, it was shown that a dynamical system subject to both periodic forcing and random perturbation may show a resonance (peak in the power spectrum) which is absent when either the forcing or the perturbations is absent.
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"A fast and efficient computer aided..." refers methods in this paper
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1,786 citations
10 Dec 2002
TL;DR: It is shown that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality and tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics.
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913 citations
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03 Aug 2016
TL;DR: In this paper, the authors present a survey of the main principles of image formation in MR imaging, including instrumentation: Magnets, Coils, and Hardware, contrast development and manipulation, and image formation.
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668 citations