Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases

Yitian Zhao; Yalin Zheng; Yifan Zhao; Yonghuai Liu; Zhili Chen; Peng Liu; Jiang Liu

doi:10.1007/978-3-030-00934-2_13

Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases

Yitian Zhao^*
, Yalin Zheng
, Yifan Zhao
, Yonghuai Liu
, Zhili Chen
, Peng Liu
, Jiang Liu

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

29 Citations (Scopus)

Abstract

Saliency is important in medical image analysis in terms of detection and segmentation tasks. We propose a new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images. The main novelty of this new saliency feature is that it is powerful in the detection of different types of lesions in different types of images without the need of tuning parameters for different problems. To evaluate its effectiveness, we have applied our method to the detection lesions of retinal images. Four different types of lesions: exudate, hemorrhage, microaneurysms and leakage from 7 independent public retinal image datasets of diabetic retinopathy and malarial retinopathy, were studied and the experimental results show that the proposed method is superior to the state-of-the-art methods.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Editors	Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
Publisher	Springer Nature
Pages	109-118
Number of pages	10
Edition	Part II
ISBN (Print)	9783030009335
DOIs	https://doi.org/10.1007/978-3-030-00934-2_13
Publication status	Published - 01 Jan 2018
Event	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 20 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11071 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16 Sept 2018 → 20 Sept 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Computer aided-diagnosis
Retinopathy
Saliency
Uniqueness

Access to Document

10.1007/978-3-030-00934-2_13

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Zhao, Y., Zheng, Y., Zhao, Y., Liu, Y., Chen, Z., Liu, P., & Liu, J. (2018). Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases. In A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, & G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (Part II ed., pp. 109-118). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11071 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-00934-2_13

Zhao, Yitian ; Zheng, Yalin ; Zhao, Yifan et al. / Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. editor / Alejandro F. Frangi ; Julia A. Schnabel ; Christos Davatzikos ; Carlos Alberola-López ; Gabor Fichtinger. Part II. ed. Springer Nature, 2018. pp. 109-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d61404627ce547209060b08405875584,

title = "Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases",

abstract = "Saliency is important in medical image analysis in terms of detection and segmentation tasks. We propose a new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images. The main novelty of this new saliency feature is that it is powerful in the detection of different types of lesions in different types of images without the need of tuning parameters for different problems. To evaluate its effectiveness, we have applied our method to the detection lesions of retinal images. Four different types of lesions: exudate, hemorrhage, microaneurysms and leakage from 7 independent public retinal image datasets of diabetic retinopathy and malarial retinopathy, were studied and the experimental results show that the proposed method is superior to the state-of-the-art methods.",

keywords = "Computer aided-diagnosis, Retinopathy, Saliency, Uniqueness",

author = "Yitian Zhao and Yalin Zheng and Yifan Zhao and Yonghuai Liu and Zhili Chen and Peng Liu and Jiang Liu",

year = "2018",

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doi = "10.1007/978-3-030-00934-2\_13",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2018",

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edition = "Part II",

note = "21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",

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Zhao, Y, Zheng, Y, Zhao, Y, Liu, Y, Chen, Z, Liu, P & Liu, J 2018, Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases. in AF Frangi, JA Schnabel, C Davatzikos, C Alberola-López & G Fichtinger (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Part II edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11071 LNCS, Springer Nature, pp. 109-118, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 16 Sept 2018. https://doi.org/10.1007/978-3-030-00934-2_13

Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases. / Zhao, Yitian; Zheng, Yalin; Zhao, Yifan et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. ed. / Alejandro F. Frangi; Julia A. Schnabel; Christos Davatzikos; Carlos Alberola-López; Gabor Fichtinger. Part II. ed. Springer Nature, 2018. p. 109-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11071 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

TY - GEN

T1 - Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases

AU - Zhao, Yitian

AU - Zheng, Yalin

AU - Zhao, Yifan

AU - Liu, Yonghuai

AU - Chen, Zhili

AU - Liu, Peng

AU - Liu, Jiang

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Saliency is important in medical image analysis in terms of detection and segmentation tasks. We propose a new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images. The main novelty of this new saliency feature is that it is powerful in the detection of different types of lesions in different types of images without the need of tuning parameters for different problems. To evaluate its effectiveness, we have applied our method to the detection lesions of retinal images. Four different types of lesions: exudate, hemorrhage, microaneurysms and leakage from 7 independent public retinal image datasets of diabetic retinopathy and malarial retinopathy, were studied and the experimental results show that the proposed method is superior to the state-of-the-art methods.

AB - Saliency is important in medical image analysis in terms of detection and segmentation tasks. We propose a new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images. The main novelty of this new saliency feature is that it is powerful in the detection of different types of lesions in different types of images without the need of tuning parameters for different problems. To evaluate its effectiveness, we have applied our method to the detection lesions of retinal images. Four different types of lesions: exudate, hemorrhage, microaneurysms and leakage from 7 independent public retinal image datasets of diabetic retinopathy and malarial retinopathy, were studied and the experimental results show that the proposed method is superior to the state-of-the-art methods.

KW - Computer aided-diagnosis

KW - Retinopathy

KW - Saliency

KW - Uniqueness

UR - https://www.scopus.com/pages/publications/85054090740

U2 - 10.1007/978-3-030-00934-2_13

DO - 10.1007/978-3-030-00934-2_13

M3 - Conference Proceeding (ISBN)

AN - SCOPUS:85054090740

SN - 9783030009335

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 109

EP - 118

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

A2 - Frangi, Alejandro F.

A2 - Schnabel, Julia A.

A2 - Davatzikos, Christos

A2 - Alberola-López, Carlos

A2 - Fichtinger, Gabor

PB - Springer Nature

T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

Y2 - 16 September 2018 through 20 September 2018

ER -

Zhao Y, Zheng Y, Zhao Y, Liu Y, Chen Z, Liu P et al. Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases. In Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Part II ed. Springer Nature. 2018. p. 109-118. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00934-2_13

Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases

Abstract

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UN SDGs

Keywords

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