Application of Edge Detection for Brain Tumor Detection

Sharma, Pratibha; Diwakar, Manoj; Choudhary, Sangam

doi:10.5120/9366-3820

Cited by 38 publications

(17 citation statements)

References 9 publications

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“…This work can also be extended to the detection and diagnosis of brain strokes by examining various brain tissue patterns. Proposed method Sharma et al [12] Karimaghaloo et al [13] PosiƟve predicƟve value (a) (b) Fig. 9.…”

Section: Discussionmentioning

confidence: 97%

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Performance analysis of classifier for brain tumor detection and diagnosis

Shanthakumar

Ganeshkumar²

2015

Computers & Electrical Engineering

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Section: Discussionmentioning

confidence: 97%

“…Fig. 9(b) presents the comparison of the proposed method with Sharma et al [12] and Karimaghaloo et al [13].…”

Section: Methodology Efmentioning

confidence: 94%

Performance analysis of classifier for brain tumor detection and diagnosis

Shanthakumar

Ganeshkumar²

2015

Computers & Electrical Engineering

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“…After segmentation-1, proceed to second segmentation which is the cellular automata edge detection [12] [15]. a) First convert the pre-processed image to binary image.…”

Section: )mentioning

confidence: 99%

An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection

Charutha¹,

Jayashree²

2014

2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT)

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Brain tumor is one of the most life-threatening diseases and hence its detection should be fast and accurate. This can be achieved by the execution of automated tumor detection techniques on medical images. Many automated techniques which use image segmentation have been proposed. Here we propose an automated and efficient brain tumor detection technique implementing on Magnetic Resonance Imaging (MRI) images, which integrates two image segmentation methods such as modified texture based region growing and cellular automata edge detection. S imulation of the proposed work is done in MATLAB. Even though the modified texture based region growing and cellular automata edge detection are efficient techniques, incorporation of both enhances the efficiency of brain tumor detection. The performance of the proposed technique is analyzed by making different comparisons. Results show that the proposed method is more efficient than modified texture based segmentation and cellular automata edge detection. From the results, it is evident that the detection by the proposed method is closer to that of the manual segmentation when it is taken as the ground truth and more dependable compared to manual segmentation. It is also understood that the modified texture based segmentation integrated with the cellular automata edge detection is better when compared to the one with the incorporation of classical edge detection methods. All these advantages make the proposed method efficient in treatment of brain tumors and also in surgical removal of tumors, if needed.

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“…But its disadvantage is that if the intensity difference between normal and tumor cells is less, it will not be detected. But it will give exact size of tumor when detected.The edge detection technique work well on high contrast images and itlacks to detect the edges in low contrast noisy images due to the weak gradient magnitude [6].…”

Section: Related Workmentioning

confidence: 99%

Detection and Segmentation of Brain Tumor by Thresholding and Bounding-Box using K-Means as a Seed

Chaitra¹,

Tale²

2017

IJCA

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Brain tumor is a mass of abnormal cells replicating in an uncontrolled manner. It affects the growth and function of normal cells in brain and occupies the space in brain. It causes interruption of brain cell function and cause damage to life. Accurate detection and segmentation of brain tumor is challenging task due to several reasons like complex brain structure, increasing data flow, inhomogeneity etc. This paper presents a novel method to segment brain tumor in T1-weighted MRI images by employing K-means followed by a thresholding technique and bounding box method by combing these different techniques accuracy of detecting the tumor portion can be increased and finally all the features of the detected tumor such as centroid, solidity, perimeter, area and segmented area and extent are extracted.

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Application of Edge Detection for Brain Tumor Detection

Cited by 38 publications

References 9 publications

Performance analysis of classifier for brain tumor detection and diagnosis

Performance analysis of classifier for brain tumor detection and diagnosis

An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection

Detection and Segmentation of Brain Tumor by Thresholding and Bounding-Box using K-Means as a Seed

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