Our research work elaborated in the design and construction of a method that bring sustenance for a reduction in false assumptions during the detection of breast cancer. Our key drive of this research work was to elude the false assumptions in the detection practice in a cost effective manner. We proposed a unique method to decrease false assumption in breast cancer detection cases and split this method in three different modules as preprocessing, formation of homogeneous blocks and color quantization. The preprocessing convoluted in eradicating the extraneous slices. The formation homogeneous blocks sub-method was to do segmentation of the image. The task of the third sub-method (i.e. color quantization) was to break the colors amid different regions. © Springer India 2016.

Debi Prasanna Acharjya

Department of Computational Intelligence

School of Computer Science and Engineering

Vellore Campus

Chiranji Lal Chowdhary

Department of Software and Systems Engineering

School of Information Technology and Engineering

Sai G.V.K

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Decrease in False Assumption for Detection Using Digital Mammography

Advances in Intelligent Systems and Computing Computational Intelligence in Data Mining—Volume 2

Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.

Journal of Biomimetics, Biomaterials and Biomedical Engineering

Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images

During past 20 years, it is stated that cancer belongings are mounting all-inclusive. Amid innumerable natures of cancers, breast cancer is witnessed as key reason of demise among women. Ultrasound, x-ray (mammograms and x-ray computed tomography), magnetic resonance imaging, thermography and nuclear medicine functional imaging are different modalities offered for early stage breast cancer detection. Mammography technology is a unadventurous breast cancer practice that can perceive tumorous masses on lower cost and better truthfulness. This paper designates the digital execution of a model, based on an intuitionistic fuzzy histogram hyperbolization and possibilitic fuzzy c-mean clustering algorithm for early breast cancer detection. Clustering plays a key role in segmentation fragment. Classical fuzzy clustering assigns data to multiple clusters at different degrees of membership but irrelevant data are also allocated to some clusters that do not relate to them. In our newfangled work we bound possibilistic method with fuzzy c-mean to resolve this issue after applying intuitionistic fuzzy histogram hyperbolization algorithm in initial preprocessing phase in the mammogram images. Further texture feature extraction technique is used for extracting features. Developed rules was applied in classifier to detect about the presence of cancerous tumor in mammogram images. The inclusive classification accuracy achieved 94% during training stage. © 2016 ACM.

Proceedings of the International Conference on Advances in Information Communication Technology & Computing - AICTC '16

Breast Cancer Detection using Intuitionistic Fuzzy Histogram Hyperbolization and Possibilitic Fuzzy c-mean Clustering algorithms with texture feature based Classification on Mammography Images

Fulltext

Procedia Computer Science

Segmentation and Feature Extraction in Medical Imaging: A Systematic Review

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Computational Intelligence in Data Mining—Volume 2
Publisher	Data powered by TypesetSpringer India
ISSN	2194-5357
Open Access	0