Application of two-dimensional fractional-order convolution and bounding box pixel analysis for rapid screening of pleural effusion

Chia Hung Lin, Chung-Dann Kan, Wei Ling Chen, Ping Tzan Huang

Research output: Contribution to journalArticle

Abstract

Pleural effusion is a pathologic symptom in which there is accumulation of body fluids around the lungs. A chest radiograph is a rapid examination technique and does not require complex setup for making a preliminary diagnosis of lung and heart diseases. In radiographic visualization, the symptom patterns appear as light or dark areas in the lung cavity. Computer-aided diagnosis is an automatic manner that can rapidly highlight the object region by preanalyzing medical images. It can improve the problems of manual inspection and allow diagnosis in remote medical facilities. Based on the ratios of lung anatomy, the automatic screening manner based on pattern recognition can be viewed as pixel value detection in the bilateral lung cavities. In this study, a fractional-order convolution (FOC) process is used to enhance the original image for an accurate extrapolation of the desired object in an image. The specific object image feature can be improved, and an accurate quantification of the pleural effusion region can be obtained using the suitable ranges of fractional-order parameters. Based on the boundaries of homogeneous regions, the pixel ratios of the lung anatomy between normal and abnormal conditions can be computed. The pleural effusion sizes and volumes can be rapidly estimated through the number of pixel changes. The experimental results reveal that the feature maps are similar and stable on image enhancement and segmentation with two fractional-order enhancement masks, as fractional-order v = 0.05 to 0.20 for mask 1# and v = 0.80 to 0.95 for mask 2#, respectively. The results also demonstrate the feasibility of the study on combining two-dimensional image FOC-process and bounding box pixel analysis to estimate the moderate and large effusion sizes from 500-2,000 mL.

Original languageEnglish
Pages (from-to)517-535
Number of pages19
JournalJournal of X-Ray Science and Technology
Volume27
Issue number3
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Pleural Effusion
Convolution
convolution integrals
lungs
boxes
Screening
screening
Pixels
pixels
Masks
Lung
masks
anatomy
Anatomy
Computer aided diagnosis
Image enhancement
Body fluids
Image Enhancement
Image segmentation
Extrapolation

All Science Journal Classification (ASJC) codes

  • Radiation
  • Instrumentation
  • Radiology Nuclear Medicine and imaging
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

Cite this

@article{c11dc5862ed04633b515d86bc5bda10a,
title = "Application of two-dimensional fractional-order convolution and bounding box pixel analysis for rapid screening of pleural effusion",
abstract = "Pleural effusion is a pathologic symptom in which there is accumulation of body fluids around the lungs. A chest radiograph is a rapid examination technique and does not require complex setup for making a preliminary diagnosis of lung and heart diseases. In radiographic visualization, the symptom patterns appear as light or dark areas in the lung cavity. Computer-aided diagnosis is an automatic manner that can rapidly highlight the object region by preanalyzing medical images. It can improve the problems of manual inspection and allow diagnosis in remote medical facilities. Based on the ratios of lung anatomy, the automatic screening manner based on pattern recognition can be viewed as pixel value detection in the bilateral lung cavities. In this study, a fractional-order convolution (FOC) process is used to enhance the original image for an accurate extrapolation of the desired object in an image. The specific object image feature can be improved, and an accurate quantification of the pleural effusion region can be obtained using the suitable ranges of fractional-order parameters. Based on the boundaries of homogeneous regions, the pixel ratios of the lung anatomy between normal and abnormal conditions can be computed. The pleural effusion sizes and volumes can be rapidly estimated through the number of pixel changes. The experimental results reveal that the feature maps are similar and stable on image enhancement and segmentation with two fractional-order enhancement masks, as fractional-order v = 0.05 to 0.20 for mask 1# and v = 0.80 to 0.95 for mask 2#, respectively. The results also demonstrate the feasibility of the study on combining two-dimensional image FOC-process and bounding box pixel analysis to estimate the moderate and large effusion sizes from 500-2,000 mL.",
author = "Lin, {Chia Hung} and Chung-Dann Kan and Chen, {Wei Ling} and Huang, {Ping Tzan}",
year = "2019",
month = "1",
day = "1",
doi = "10.3233/XST-180473",
language = "English",
volume = "27",
pages = "517--535",
journal = "Journal of X-Ray Science and Technology",
issn = "0895-3996",
publisher = "IOS Press",
number = "3",

}

Application of two-dimensional fractional-order convolution and bounding box pixel analysis for rapid screening of pleural effusion. / Lin, Chia Hung; Kan, Chung-Dann; Chen, Wei Ling; Huang, Ping Tzan.

In: Journal of X-Ray Science and Technology, Vol. 27, No. 3, 01.01.2019, p. 517-535.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Application of two-dimensional fractional-order convolution and bounding box pixel analysis for rapid screening of pleural effusion

AU - Lin, Chia Hung

AU - Kan, Chung-Dann

AU - Chen, Wei Ling

AU - Huang, Ping Tzan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Pleural effusion is a pathologic symptom in which there is accumulation of body fluids around the lungs. A chest radiograph is a rapid examination technique and does not require complex setup for making a preliminary diagnosis of lung and heart diseases. In radiographic visualization, the symptom patterns appear as light or dark areas in the lung cavity. Computer-aided diagnosis is an automatic manner that can rapidly highlight the object region by preanalyzing medical images. It can improve the problems of manual inspection and allow diagnosis in remote medical facilities. Based on the ratios of lung anatomy, the automatic screening manner based on pattern recognition can be viewed as pixel value detection in the bilateral lung cavities. In this study, a fractional-order convolution (FOC) process is used to enhance the original image for an accurate extrapolation of the desired object in an image. The specific object image feature can be improved, and an accurate quantification of the pleural effusion region can be obtained using the suitable ranges of fractional-order parameters. Based on the boundaries of homogeneous regions, the pixel ratios of the lung anatomy between normal and abnormal conditions can be computed. The pleural effusion sizes and volumes can be rapidly estimated through the number of pixel changes. The experimental results reveal that the feature maps are similar and stable on image enhancement and segmentation with two fractional-order enhancement masks, as fractional-order v = 0.05 to 0.20 for mask 1# and v = 0.80 to 0.95 for mask 2#, respectively. The results also demonstrate the feasibility of the study on combining two-dimensional image FOC-process and bounding box pixel analysis to estimate the moderate and large effusion sizes from 500-2,000 mL.

AB - Pleural effusion is a pathologic symptom in which there is accumulation of body fluids around the lungs. A chest radiograph is a rapid examination technique and does not require complex setup for making a preliminary diagnosis of lung and heart diseases. In radiographic visualization, the symptom patterns appear as light or dark areas in the lung cavity. Computer-aided diagnosis is an automatic manner that can rapidly highlight the object region by preanalyzing medical images. It can improve the problems of manual inspection and allow diagnosis in remote medical facilities. Based on the ratios of lung anatomy, the automatic screening manner based on pattern recognition can be viewed as pixel value detection in the bilateral lung cavities. In this study, a fractional-order convolution (FOC) process is used to enhance the original image for an accurate extrapolation of the desired object in an image. The specific object image feature can be improved, and an accurate quantification of the pleural effusion region can be obtained using the suitable ranges of fractional-order parameters. Based on the boundaries of homogeneous regions, the pixel ratios of the lung anatomy between normal and abnormal conditions can be computed. The pleural effusion sizes and volumes can be rapidly estimated through the number of pixel changes. The experimental results reveal that the feature maps are similar and stable on image enhancement and segmentation with two fractional-order enhancement masks, as fractional-order v = 0.05 to 0.20 for mask 1# and v = 0.80 to 0.95 for mask 2#, respectively. The results also demonstrate the feasibility of the study on combining two-dimensional image FOC-process and bounding box pixel analysis to estimate the moderate and large effusion sizes from 500-2,000 mL.

UR - http://www.scopus.com/inward/record.url?scp=85068548898&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068548898&partnerID=8YFLogxK

U2 - 10.3233/XST-180473

DO - 10.3233/XST-180473

M3 - Article

VL - 27

SP - 517

EP - 535

JO - Journal of X-Ray Science and Technology

JF - Journal of X-Ray Science and Technology

SN - 0895-3996

IS - 3

ER -