An Intuitionistic Fuzzy Clustering Approach for Detection of Abnormal Regions in Mammogram Images.
Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very crucial if the mammogram images are blurred or low c...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 2; pp. 428 - 440 |
|---|---|
| Autor principal: | |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151472690&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151472690 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2021 vid: 34 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151472690 149439939 151472690 151472690 10.1007/s10278-021-00444-3 151472690 ppf: 428 ppct: 12 formats: fmt: @attributes: type: P tig: atl: An Intuitionistic Fuzzy Clustering Approach for Detection of Abnormal Regions in Mammogram Images. aug: au: Chaira, Tamalika affil: Aravali Pharma and Lifesciences, 110075, New Delhi, India sug: subj: Breast Neoplasms Diagnosis Mammography Methods Image Processing, Computer Assisted Methods Logic Algorithms Evaluation Human Diagnosis, Computer Assisted Software Design Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted ab: Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very crucial if the mammogram images are blurred or low contrast. This paper suggests a novel clustering approach for segmenting lesions/tumors in the mammogram images using Atanassov's intuitionistic fuzzy set theory. The algorithm initially converts an image to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. From the intuitionistic fuzzy image, two membership intervals are computed. Then, using Zadeh's min t-conorm, a new membership function is computed. Using the new membership function, an interval type 2 fuzzy image is constructed. Two types of distance functions are used in clustering—intuitionistic fuzzy divergence and a fuzzy exponential type distance function. Further, in each iteration, membership matrix is updated using a hesitation degree and a clustered image is obtained. Tumors/lesions are then segmented from the clustered image. The proposed method is compared with existing methods both quantitatively and qualitatively and it is observed that the proposed method performs better than the existing methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|