Image segmentation using hierarchical analysis of 2D-histograms - Application to medical images

Zennouhi, Rachid; Masmoudi, Lhoussaine; Ansari, Mohamed El

doi:10.1109/mmcs.2009.5256647

Cited by 2 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Segmentation or clustering significantly simplifies the extraction of color, structure, shape and texture information. These clustering approaches are widely categorized into two groups: hierarchical and partitional clustering [2,3]. The first category renders a hierarchical cluster tree, referred to as a dendrogram, that might be either divisive or agglomerative [2].…”

Section: Introductionmentioning

confidence: 99%

“…These clustering approaches are widely categorized into two groups: hierarchical and partitional clustering [2,3]. The first category renders a hierarchical cluster tree, referred to as a dendrogram, that might be either divisive or agglomerative [2]. The second category contributes a unitary partition of objects by a pre-specified number of clusters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

Srinivas*,

Reddy

2019

IJEAT

View full text Add to dashboard Cite

The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis

show abstract

Section: Introductionmentioning

confidence: 99%