Interval type-2 fuzzy image processing expert system for diagnosing brain tumors

Zarinbal, Marzieh; Turksen, L.B.; Zarandi, M. H. Fazel; Izadid, M.

doi:10.1109/norbert.2014.6893890

Cited by 5 publications

(7 citation statements)

References 16 publications

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“…Zarinbal et al 15 used IF2FS for diagnosing brain tumors. The algorithm developed assists the radiologist in handling uncertainties such as poor lighting condition and nonlinearity of input MR brain images.…”

Section: R Elat Ed W Ork Smentioning

confidence: 99%

“…With respect to each primary membership, there is an existence of secondary membership that briefs the possible formulation of primary membership, which can also lie between 0 and 1. 15,23,24 The concept of uncertainty is characterized by a region and it is notified as the Foot Print of Uncertainty (FOU). When we describe lÃ x; u ð Þ51; 8u 2 J x ½0; 1, we can obtain an interval type-2 membership function.…”

Section: Interval Type-2 Fuzzy C-meansmentioning

confidence: 99%

“…FOU helps in representing the entire IF2FS and it can be termed using both upper membership function l A ðxÞ and lower membership function l A ðxÞ. 7,15,23,25 In IT2FCM, lower and upper memberships are engaged to manage the uncertainty of fuzzifier "m." The fuzzifiers "m 1 " and "m 2 " fuzzify the FOU of single fuzzifier value "m." Upper membership function l A ðxÞ and lower membership function l A ðxÞ are derived as specified in Equations 7 and 8.…”

Section: Interval Type-2 Fuzzy C-meansmentioning

confidence: 99%

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A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Alagarsamy

Kamatchi

Govindaraj

et al. 2017

Int J Imaging Syst Tech

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This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.

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Section: R Elat Ed W Ork Smentioning

confidence: 99%

Section: Interval Type-2 Fuzzy C-meansmentioning

confidence: 99%

Section: Interval Type-2 Fuzzy C-meansmentioning

confidence: 99%

See 1 more Smart Citation

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Alagarsamy

Kamatchi

Govindaraj

et al. 2017

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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“…They showed that the high uncertainty of some clinical symptoms required more accuracy to get acceptable results. Zarinbal et al [19,20] developed a type-2 fuzzy image processing expert system to diagnose brain tumors. They evaluated the system performance using 95 MRI scans, showing good capacity of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

et al. 2018

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The majority of people have experienced pain in their low back or neck in their lives. In this paper, a type-2 fuzzy rule-based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in ve categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Use of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians' diagnosis. The outputs of the system are comprised of type of disorder, location, and severity as well as the necessity of taking an M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

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“…967. M. Zarinbal [10] work in astrocytomas extraction only using type 2 fuzzy and have got accuracy of 89%.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation using hybrid of both Netrosopic Modified Nonlocal Fuzzy C-mean and Modified Level Sets

2016

IJSR

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an improved segmentation approach based on Neutrosophic sets (NS) and Modified Non local Fuzzy c-mean clustering (NLFCM) is proposed. The brain tumor MRI image is transformed into NS domain, which is described using three subsets namely; the percentage of truth in a subset T%, the percentage of indeterminacy in a subset I% , and the percentage of falsity in a subset F% . The entropy in NS is defined and employed to evaluate the indeterminacy. NS image is adapted also using Modified Non local Fuzzy Cmean algorithm(MNLFCM). Finally, MRI brain tumor image is segmented and tumor is selected using Modified Level Sets (MLS). The proposed approach denoted as NS-MNLFCM-MLS and compared with another paper using Jaccard Index and Dice Coefficient. The experimental results demonstrate that the proposed approach is less sensitive to noise and performs better on MRI brain image.

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Interval type-2 fuzzy image processing expert system for diagnosing brain tumors

Cited by 5 publications

References 16 publications

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

Brain Tumor Segmentation using hybrid of both Netrosopic Modified Nonlocal Fuzzy C-mean and Modified Level Sets

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