Positional Connected Component Labeling Algorithm

Devi, G. Gayathri

doi:10.17485/ijst/2014/v7i3.5

Cited by 6 publications

(2 citation statements)

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“…In order to improve and extract the texture encoding, Ojala et al [17] proposed an extension to the original operator called uniform LBP patterns (ULBP). The uniformity of the LBP is an important concept to represent local primitive structural information such as edges and corners [20]. When the bit pattern is considered circular, a uniformity measure of a pattern counts the bitwise jumps from 0 to 1 and from 1 to 0.…”

Section: Local Binary Patternmentioning

confidence: 99%

“…This is obtained by scanning all pixels in the binary image and characterizing all the connected portions. The obtained image will have two regions; the foreground object that is occupied by pixels with 1 value, and the background portion that is occupied by pixels with 0 value [20]. In many applications, not all the connected components are important.…”

Section: Connected Component Labelingmentioning

confidence: 99%

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K-mean clustering and local binary pattern techniques for automatic brain tumor detection

Baji¹,

Abdullah²,

Abdulsattar³

2023

Bulletin EEI

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Tumors in brains are caused by the unregulated emergence of tissue cells inside the brain. The early diagnosis and determining the precise location of the tumor in magnetic resonance imaging (MRI) and its size are essential for the teams of physicians. Image segmentation is often considered a preliminary step in medical image analyses. K-means clustering has been widely adopted for brain tumor detection. The result of this technique is a list of cluster images. The challenge of this method is the difficulty of selecting the appropriate cluster section that depicts the tumor. In this work, we analyze the influence of different image clusters. Each cluster is then split into the left and right parts. After that, the texture features are depicted in each part. Furthermore, the bilateral symmetry measure is applied to estimate the cluster that contains the tumor. Finally, the connected component labeling is employed to determine the target cluster for brain tumor detection. The developed technique is applied to 30 MRI images. The encouraging accuracy of 87% is obtained.

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Section: Local Binary Patternmentioning

confidence: 99%

Section: Connected Component Labelingmentioning

confidence: 99%