Medical ultrasound image similarity measurement by human visual system (HVS) modeling

H H Bulthoff; S W Lee; T A Poggio; C Wallraven

Medical ultrasound image similarity measurement by human visual system (HVS) modeling

H H Bulthoff (Editor), S W Lee (Editor), T A Poggio (Editor), C Wallraven

King's College London

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

Abstract

We applied a four stage model of human vision to the problem of similarity measurement of medical (liver) ultrasound images. The results showed that when comparing a given image to a set that contained images with similar features, the model was able to correctly identify the most similar image in the set. Additionally, the shape of the similarity function closely followed a subjective measure of visual similarity for images around the best match. Removing some computational steps to reduce processing time enabled the comparison method to run in near real-time ([left angle bracket] 5 seconds), but with some acceptable loss of accuracy. These results could not be achieved using conventional similarity measurements based on image grey level statistics. (26 References).

Original language	English
Title of host publication	Conference Proceedings - Lecture Notes in Computer Science (LNCS) Vol# 2525
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	340 - 347
Number of pages	8
Publication status	Published - 2002
Event	BMCV 2002: Biologically Motivated Computer Vision - 2nd International Workshop - Tubingen, Germany Duration: 22 Nov 2002 → 24 Nov 2002

Conference

Conference	BMCV 2002: Biologically Motivated Computer Vision - 2nd International Workshop
Country/Territory	Germany
City	Tubingen
Period	22/11/2002 → 24/11/2002

Cite this

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title = "Medical ultrasound image similarity measurement by human visual system (HVS) modeling",

abstract = "We applied a four stage model of human vision to the problem of similarity measurement of medical (liver) ultrasound images. The results showed that when comparing a given image to a set that contained images with similar features, the model was able to correctly identify the most similar image in the set. Additionally, the shape of the similarity function closely followed a subjective measure of visual similarity for images around the best match. Removing some computational steps to reduce processing time enabled the comparison method to run in near real-time ([left angle bracket] 5 seconds), but with some acceptable loss of accuracy. These results could not be achieved using conventional similarity measurements based on image grey level statistics. (26 References).",

author = "Bulthoff, {H H} and Lee, {S W} and Poggio, {T A} and C Wallraven",

year = "2002",

language = "English",

pages = "340 -- 347",

booktitle = "Conference Proceedings - Lecture Notes in Computer Science (LNCS) Vol# 2525",

publisher = "Springer",

note = "BMCV 2002: Biologically Motivated Computer Vision - 2nd International Workshop ; Conference date: 22-11-2002 Through 24-11-2002",

}

Bulthoff, HH (ed.), Lee, SW (ed.), Poggio, TA (ed.) & Wallraven, C 2002, Medical ultrasound image similarity measurement by human visual system (HVS) modeling. in Conference Proceedings - Lecture Notes in Computer Science (LNCS) Vol# 2525. Springer, Berlin, Germany, pp. 340 - 347, BMCV 2002: Biologically Motivated Computer Vision - 2nd International Workshop, Tubingen, Germany, 22/11/2002.

TY - CHAP

T1 - Medical ultrasound image similarity measurement by human visual system (HVS) modeling

AU - Wallraven, C

A2 - Bulthoff, H H

A2 - Lee, S W

A2 - Poggio, T A

PY - 2002

Y1 - 2002

N2 - We applied a four stage model of human vision to the problem of similarity measurement of medical (liver) ultrasound images. The results showed that when comparing a given image to a set that contained images with similar features, the model was able to correctly identify the most similar image in the set. Additionally, the shape of the similarity function closely followed a subjective measure of visual similarity for images around the best match. Removing some computational steps to reduce processing time enabled the comparison method to run in near real-time ([left angle bracket] 5 seconds), but with some acceptable loss of accuracy. These results could not be achieved using conventional similarity measurements based on image grey level statistics. (26 References).

AB - We applied a four stage model of human vision to the problem of similarity measurement of medical (liver) ultrasound images. The results showed that when comparing a given image to a set that contained images with similar features, the model was able to correctly identify the most similar image in the set. Additionally, the shape of the similarity function closely followed a subjective measure of visual similarity for images around the best match. Removing some computational steps to reduce processing time enabled the comparison method to run in near real-time ([left angle bracket] 5 seconds), but with some acceptable loss of accuracy. These results could not be achieved using conventional similarity measurements based on image grey level statistics. (26 References).

M3 - Conference paper

SP - 340

EP - 347

BT - Conference Proceedings - Lecture Notes in Computer Science (LNCS) Vol# 2525

PB - Springer

CY - Berlin, Germany

T2 - BMCV 2002: Biologically Motivated Computer Vision - 2nd International Workshop

Y2 - 22 November 2002 through 24 November 2002

ER -

Medical ultrasound image similarity measurement by human visual system (HVS) modeling

Abstract

Conference

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