Segmentation of ultrasound images by using an incremental self-organized map

M. N. Kurnaz; Z. Dokur; T. Ölmez

Segmentation of ultrasound images by using an incremental self-organized map

M. N. Kurnaz^*, Z. Dokur, T. Ölmez

^*Bu çalışma için yazışmadan sorumlu yazar

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma sonucu: Dergiye katkı › Konferans makalesi › bilirkişi

8 Atıf (Scopus)

Özet

This paper presents a new segmentation method for ultrasound images. A new incremental self-organized map is proposed for the segmentation of the ultrasound images. Elements of the feature vectors are formed by the fast Fourier transform (FFT) of image intensities in 4×4 square blocks. In this study, two neural networks for segmentation are comparatively examined: Kohonen map, and incremental self-organized map (ISOM). It is observed that ISOM gives the best classification performance with less number of nodes after a short training time.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	2638-2640
Sayfa sayısı	3
Dergi	Annual Reports of the Research Reactor Institute, Kyoto University
Hacim	3
Yayın durumu	Yayınlandı - 2001
Etkinlik	23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey Süre: 25 Eki 2001 → 28 Eki 2001

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Link to publication in Scopus

Alıntı Yap

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title = "Segmentation of ultrasound images by using an incremental self-organized map",

abstract = "This paper presents a new segmentation method for ultrasound images. A new incremental self-organized map is proposed for the segmentation of the ultrasound images. Elements of the feature vectors are formed by the fast Fourier transform (FFT) of image intensities in 4×4 square blocks. In this study, two neural networks for segmentation are comparatively examined: Kohonen map, and incremental self-organized map (ISOM). It is observed that ISOM gives the best classification performance with less number of nodes after a short training time.",

keywords = "Classification, Multilayer perceptron, Neural networks, Segmentation of biomedical images, Self organized map",

author = "Kurnaz, {M. N.} and Z. Dokur and T. {\"O}lmez",

year = "2001",

language = "English",

volume = "3",

pages = "2638--2640",

journal = "Annual Reports of the Research Reactor Institute, Kyoto University",

issn = "0454-9244",

note = "23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 25-10-2001 Through 28-10-2001",

}

TY - JOUR

T1 - Segmentation of ultrasound images by using an incremental self-organized map

AU - Kurnaz, M. N.

AU - Dokur, Z.

AU - Ölmez, T.

PY - 2001

Y1 - 2001

N2 - This paper presents a new segmentation method for ultrasound images. A new incremental self-organized map is proposed for the segmentation of the ultrasound images. Elements of the feature vectors are formed by the fast Fourier transform (FFT) of image intensities in 4×4 square blocks. In this study, two neural networks for segmentation are comparatively examined: Kohonen map, and incremental self-organized map (ISOM). It is observed that ISOM gives the best classification performance with less number of nodes after a short training time.

AB - This paper presents a new segmentation method for ultrasound images. A new incremental self-organized map is proposed for the segmentation of the ultrasound images. Elements of the feature vectors are formed by the fast Fourier transform (FFT) of image intensities in 4×4 square blocks. In this study, two neural networks for segmentation are comparatively examined: Kohonen map, and incremental self-organized map (ISOM). It is observed that ISOM gives the best classification performance with less number of nodes after a short training time.

KW - Classification

KW - Multilayer perceptron

KW - Neural networks

KW - Segmentation of biomedical images

KW - Self organized map

UR - http://www.scopus.com/inward/record.url?scp=0035782015&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0035782015

SN - 0454-9244

VL - 3

SP - 2638

EP - 2640

JO - Annual Reports of the Research Reactor Institute, Kyoto University

JF - Annual Reports of the Research Reactor Institute, Kyoto University

T2 - 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 25 October 2001 through 28 October 2001

ER -

Segmentation of ultrasound images by using an incremental self-organized map

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