e-journal

Automated Segmentation of the Melanocytes in Skin Histopathological Images

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Abstract—In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of melanocytes in the epidermis area is difficult because other keratinocytesthatareverysimilartothemelanocytesarealsopresent. Thispaperproposesanovelcomputer-aidedtechniqueforsegmentation of the melanocytes in the skin histopathological images. In order to reduce the local intensity variant, a mean-shift algorithm is applied for the initial segmentation of the image. A local region recursive segmentation algorithm is then proposed to filter outthecandidatenucleiregionsbasedonthedomainpriorknowledge. To distinguish the melanocytes from other keratinocytes in the epidermis area, a novel descriptor, named local double ellipse descriptor(LDED),isproposedtomeasurethelocalfeaturesofthe candidateregions.TheLDEDusestwoparameters:regionellipticity and local pattern characteristics to distinguishthe melanocytes from the candidate nuclei regions. Experimental results on 28 different histopathological images of skin tissue with different zooming factors show that the proposed technique provides a superior performance.

Index Terms—Histopathological image analysis, image segmentation, local descriptor, object detection, pattern recognition.

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Ketersediaan

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Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013

Deskripsi Fisik

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 2, MARCH 2013 p. 284-295

Bahasa

English

ISBN/ISSN

2168-2194

Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

HISTOLOGI PATOLOGIS

Info Detail Spesifik

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Pernyataan Tanggungjawab

Cheng Lu ... [et al.]

Versi lain/terkait

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