Open AccessModified spatial neutrosophic clustering technique for boundary extraction of tumours in B‐mode BUS images
Author(s) -
Lal Madan,
Kaur Lakhwinder,
Gupta Savita
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0466
Subject(s) - cluster analysis , computer science , artificial intelligence , pattern recognition (psychology) , segmentation , fuzzy clustering , fuzzy logic , image segmentation , computer vision , data mining
Breast ultrasound (BUS) images are of poor quality, contain inherent noise and shadow regions. Consequently, the task of tumour segmentation from these images becomes more difficult. In this study, a modified spatial neutrosophic clustering technique has been proposed for automatic boundary extraction of tumours in B‐mode BUS images. The contributions of the work are two‐fold: (i) spatial information is incorporated in the neutrosophic ℓ ‐means (NLM) clustering method for better cluster formation and (ii) membership values are updated by using type‐2 membership function, which helps in converging the cluster centres to more desirable locations than ordinary fuzzy membership functions. BUS images with manually marked lesions by an experienced radiologist have been used as gold standard/reference images for quantitative comparison. The proposed method has been applied to 60 BUS images and results are recorded in the form of area and boundary error metrics. The performance of the proposed method has been compared with the region growing, fuzzy c‐means clustering, watershed segmentation, neutrosophic c‐means clustering and NLM clustering methods. From the quantitative and visual results, it has been observed that the proposed method can extract the tumour boundaries more precisely as compared with the other state‐of‐the‐art clustering techniques.