Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models

Nazre Batool

doi:10.1109/IPTA.2016.7820969

Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models

Nazre Batool

KTH Royal Institute of Technology

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

11 Citations (Scopus)

Abstract

Hepatic steatosis is a defining feature of nonalcoholic fatty liver disease, emerging with the increasing incidence of obesity and metabolic syndrome. The research in image-based analysis of hepatic steatosis mostly focuses on the quantification of fat in biopsy images. This work furthers the image-based analysis of hepatic steatosis by exploring the spatial characteristics of fat globules in whole slide biopsy images after performing fat detection. An algorithm based on morphological filtering and sparse linear models is presented for fat detection. Then the spatial properties of detected fat globules in relation to the hepatic anatomical structures of central veins and portal tracts are explored. The test dataset consists of 38 high resolution images from 21 patients. The experimental results provide an insight into the size distributions of fat globules and their location with respect to the anatomical structures.

Original language	English
Title of host publication	2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016
Editors	Matti Pietikainen, Abdenour Hadid, Miguel Bordallo Lopez
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467389105
DOIs	https://doi.org/10.1109/IPTA.2016.7820969
Publication status	Published - 17 Jan 2017
Externally published	Yes
Event	6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 - Oulu, Finland Duration: 12 Dec 2016 → 15 Dec 2016

Publication series

Name	2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016

Conference

Conference	6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016
Country/Territory	Finland
City	Oulu
Period	12/12/16 → 15/12/16

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

biopsy image analysis
dictionary-based algorithm
digital pathology
Hepatic steatosis
liver fat detection
nonalcoholic fatty liver disease
shape classification
sparse linear models
spatial analysis

Access to Document

10.1109/IPTA.2016.7820969

Cite this

Batool, N. (2017). Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models. In M. Pietikainen, A. Hadid, & M. B. Lopez (Eds.), 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 Article 7820969 (2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPTA.2016.7820969

Batool, Nazre. / Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models. 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016. editor / Matti Pietikainen ; Abdenour Hadid ; Miguel Bordallo Lopez. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016).

@inproceedings{708d44525b2e4b0fa2f17acd7418aa77,

title = "Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models",

abstract = "Hepatic steatosis is a defining feature of nonalcoholic fatty liver disease, emerging with the increasing incidence of obesity and metabolic syndrome. The research in image-based analysis of hepatic steatosis mostly focuses on the quantification of fat in biopsy images. This work furthers the image-based analysis of hepatic steatosis by exploring the spatial characteristics of fat globules in whole slide biopsy images after performing fat detection. An algorithm based on morphological filtering and sparse linear models is presented for fat detection. Then the spatial properties of detected fat globules in relation to the hepatic anatomical structures of central veins and portal tracts are explored. The test dataset consists of 38 high resolution images from 21 patients. The experimental results provide an insight into the size distributions of fat globules and their location with respect to the anatomical structures.",

keywords = "biopsy image analysis, dictionary-based algorithm, digital pathology, Hepatic steatosis, liver fat detection, nonalcoholic fatty liver disease, shape classification, sparse linear models, spatial analysis",

author = "Nazre Batool",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 ; Conference date: 12-12-2016 Through 15-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/IPTA.2016.7820969",

language = "English",

series = "2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Matti Pietikainen and Abdenour Hadid and Lopez, \{Miguel Bordallo\}",

booktitle = "2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016",

}

Batool, N 2017, Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models. in M Pietikainen, A Hadid & MB Lopez (eds), 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016., 7820969, 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, Institute of Electrical and Electronics Engineers Inc., 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, Oulu, Finland, 12/12/16. https://doi.org/10.1109/IPTA.2016.7820969

Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models. / Batool, Nazre.
2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016. ed. / Matti Pietikainen; Abdenour Hadid; Miguel Bordallo Lopez. Institute of Electrical and Electronics Engineers Inc., 2017. 7820969 (2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models

AU - Batool, Nazre

PY - 2017/1/17

Y1 - 2017/1/17

N2 - Hepatic steatosis is a defining feature of nonalcoholic fatty liver disease, emerging with the increasing incidence of obesity and metabolic syndrome. The research in image-based analysis of hepatic steatosis mostly focuses on the quantification of fat in biopsy images. This work furthers the image-based analysis of hepatic steatosis by exploring the spatial characteristics of fat globules in whole slide biopsy images after performing fat detection. An algorithm based on morphological filtering and sparse linear models is presented for fat detection. Then the spatial properties of detected fat globules in relation to the hepatic anatomical structures of central veins and portal tracts are explored. The test dataset consists of 38 high resolution images from 21 patients. The experimental results provide an insight into the size distributions of fat globules and their location with respect to the anatomical structures.

AB - Hepatic steatosis is a defining feature of nonalcoholic fatty liver disease, emerging with the increasing incidence of obesity and metabolic syndrome. The research in image-based analysis of hepatic steatosis mostly focuses on the quantification of fat in biopsy images. This work furthers the image-based analysis of hepatic steatosis by exploring the spatial characteristics of fat globules in whole slide biopsy images after performing fat detection. An algorithm based on morphological filtering and sparse linear models is presented for fat detection. Then the spatial properties of detected fat globules in relation to the hepatic anatomical structures of central veins and portal tracts are explored. The test dataset consists of 38 high resolution images from 21 patients. The experimental results provide an insight into the size distributions of fat globules and their location with respect to the anatomical structures.

KW - biopsy image analysis

KW - dictionary-based algorithm

KW - digital pathology

KW - Hepatic steatosis

KW - liver fat detection

KW - nonalcoholic fatty liver disease

KW - shape classification

KW - sparse linear models

KW - spatial analysis

UR - https://www.scopus.com/pages/publications/85013187947

U2 - 10.1109/IPTA.2016.7820969

DO - 10.1109/IPTA.2016.7820969

M3 - Conference Publication

AN - SCOPUS:85013187947

T3 - 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016

BT - 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016

A2 - Pietikainen, Matti

A2 - Hadid, Abdenour

A2 - Lopez, Miguel Bordallo

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016

Y2 - 12 December 2016 through 15 December 2016

ER -

Batool N. Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models. In Pietikainen M, Hadid A, Lopez MB, editors, 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820969. (2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016). doi: 10.1109/IPTA.2016.7820969

Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this