Tumor Demarcation in Mammography Images using LBG on Probability Image

Kekre, H. B.; Gharge, Saylee; Sarode, Tanuja

doi:10.5120/747-931

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…When the bone marrow generates abnormal white blood cells, they are known as the cancerous blood cells [1] which may be lymphocytic or myelogenous. The cancerous blood cells can be either acute or chronic.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Segmentation of Acute Leukemia Cells

Kandil¹,

Hassan²

2016

IJCA

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The recognition of the acute Leukemia blast cells in colored microscopic images is a challenging task. Segmentation is the essential step for image analysis and image processing. In this paper, an algorithm is presented that consists of panel selection followed by segmentation using K-means clustering then a refinement process. This algorithm was applied on public dataset designed for testing segmentation techniques. The results were compared with two different segmentation techniques developed by other researchers on the same data set. Our algorithm results in a sensitivity of 97.4 % and specificity of 98.1%. The developed algorithm was tested to another dataset of samples extracted from patients in local hospitals. The algorithm results in sensitivity of 100%, Specificity of 99.747% and accuracy of 99.7617%. The results were approved by expert pathologists. General TermsPattern Recognition, Image processing and segmentation.

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Section: Introductionmentioning

confidence: 99%

“…The code vector Ci = {Ci1,…, Cik}; i = 1, 2, ..., k, represents the codebook [1,12,[16][17][18]. H. B. Kekre et al stated that good codebook is the key to successful VQ [15,[19][20].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Acute Leukemia Cells

Kandil¹,

Hassan²

2016

IJCA

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Segmentation of Blast using Vector Quantization Technique

Kekre¹,

Patankar²,

Galiyal³

2013

IJCA

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Image enhancement plays an important role in computer vision and image processing. Leukemia is a malignant disease (cancer) seen in people of any age groups either in children or adults aged over 50 years. It is characterized by the uncontrolled accumulation of immature white blood cells. Further the noises and blurriness effect often lead to false diagnosis of leukemia .The recognition of acute leukemia blood cell based on color image is one of the most challenging tasks in image processing. Also, the conventional method of manual counting using a microscope is a time consuming, produces errors and put an intolerable amount of stress to technicians. As a solution to this problem, this paper proposed vector quantization technique for segmentation of blast in acute leukemia images. This method is applied on 115 microscopic images and succeeds with specificity of 90% and sensitivity of 60% to detect abnormal white blood cells (blast).Images used are availabe at

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Tumor Demarcation in Mammography Images using LBG on Probability Image

Cited by 2 publications

References 41 publications

Automatic Segmentation of Acute Leukemia Cells

Automatic Segmentation of Acute Leukemia Cells

Segmentation of Blast using Vector Quantization Technique

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