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Title: Fractal Analysis of Visual Search Activity for Mass Detection During Mammographic Screening

Abstract

Purpose: The objective of this study was to assess the complexity of human visual search activity during mammographic screening using fractal analysis and to investigate its relationship with case and reader characteristics. Methods: The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) and 10 readers (three board certified radiologists and seven radiology residents), formed the corpus data for this study. The fractal dimension of the readers’ visual scanning patterns was computed with the Minkowski–Bouligand box-counting method and used as a measure of gaze complexity. Individual factor and group-based interaction ANOVA analysis was performed to study the association between fractal dimension, case pathology, breast density, and reader experience level. The consistency of the observed trends depending on gaze data representation was also examined. Results: Case pathology, breast density, reader experience level, and individual reader differences are all independent predictors of the visual scanning pattern complexity when screening for breast cancer. No higher order effects were found to be significant. Conclusions: Fractal characterization of visual search behavior during mammographic screening ismore » dependent on case properties and image reader characteristics.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2];  [2];  [3]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Health Data Sciences Institute
  2. Univ. of Tennessee Medical Center, Knoxville, TN (United States). Dept. of Radiology
  3. Texas A & M Univ., College Station, TX (United States). Dept. of Computer Science and Engineering
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1393890
Grant/Contract Number:
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 44; Journal Issue: 3; Journal ID: ISSN 0094-2405
Publisher:
American Association of Physicists in Medicine
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Alamudun, Folami T., Yoon, Hong-Jun, Hudson, Kathy, Morin-Ducote, Garnetta, Hammond, Tracy, and Tourassi, Georgia. Fractal Analysis of Visual Search Activity for Mass Detection During Mammographic Screening. United States: N. p., 2017. Web. doi:10.1002/mp.12100.
Alamudun, Folami T., Yoon, Hong-Jun, Hudson, Kathy, Morin-Ducote, Garnetta, Hammond, Tracy, & Tourassi, Georgia. Fractal Analysis of Visual Search Activity for Mass Detection During Mammographic Screening. United States. doi:10.1002/mp.12100.
Alamudun, Folami T., Yoon, Hong-Jun, Hudson, Kathy, Morin-Ducote, Garnetta, Hammond, Tracy, and Tourassi, Georgia. Tue . "Fractal Analysis of Visual Search Activity for Mass Detection During Mammographic Screening". United States. doi:10.1002/mp.12100. https://www.osti.gov/servlets/purl/1393890.
@article{osti_1393890,
title = {Fractal Analysis of Visual Search Activity for Mass Detection During Mammographic Screening},
author = {Alamudun, Folami T. and Yoon, Hong-Jun and Hudson, Kathy and Morin-Ducote, Garnetta and Hammond, Tracy and Tourassi, Georgia},
abstractNote = {Purpose: The objective of this study was to assess the complexity of human visual search activity during mammographic screening using fractal analysis and to investigate its relationship with case and reader characteristics. Methods: The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) and 10 readers (three board certified radiologists and seven radiology residents), formed the corpus data for this study. The fractal dimension of the readers’ visual scanning patterns was computed with the Minkowski–Bouligand box-counting method and used as a measure of gaze complexity. Individual factor and group-based interaction ANOVA analysis was performed to study the association between fractal dimension, case pathology, breast density, and reader experience level. The consistency of the observed trends depending on gaze data representation was also examined. Results: Case pathology, breast density, reader experience level, and individual reader differences are all independent predictors of the visual scanning pattern complexity when screening for breast cancer. No higher order effects were found to be significant. Conclusions: Fractal characterization of visual search behavior during mammographic screening is dependent on case properties and image reader characteristics.},
doi = {10.1002/mp.12100},
journal = {Medical Physics},
number = 3,
volume = 44,
place = {United States},
year = {Tue Feb 21 00:00:00 EST 2017},
month = {Tue Feb 21 00:00:00 EST 2017}
}

Journal Article:
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  • This paper presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The DWCE enhances structures within the digitized mammogram so that a simple edge detection algorithm can be used to define the boundaries of the objectives. Once the object boundaries are known, morphological features are extracted and used by a classification algorithm to differentiate regions within the image. This paper introduces the DWCE algorithm and presents results of a preliminary study based on 25 digitized mammograms with biopsy proven masses.more » It also compares morphological feature classification based on sequential thresholding, linear discriminant analysis, and neural network classifiers for reduction of false-positive detections.« less
  • Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-ordermore » spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be A{sub z}=0.783{+-}0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.« less
  • A method is presented to improve computer aided detection (CAD) results for masses in mammograms by fusing information obtained from two views of the same breast. It is based on a previously developed approach to link potentially suspicious regions in mediolateral oblique (MLO) and craniocaudal (CC) views. Using correspondence between regions, we extended our CAD scheme by building a cascaded multiple-classifier system, in which the last stage computes suspiciousness of an initially detected region conditional on the existence and similarity of a linked candidate region in the other view. We compared the two-view detection system with the single-view detection methodmore » using free-response receiver operating characteristic (FROC) analysis and cross validation. The dataset used in the evaluation consisted of 948 four-view mammograms, including 412 cancer cases with a mass, architectural distortion, or asymmetry. A statistically significant improvement was found in the lesion based detection performance. At a false positive (FP) rate of 0.1 FP/image, the lesion sensitivity improved from 56% to 61%. Case based sensitivity did not improve.« less
  • The effect of reduction in dose levels normally used in mammographic screening procedures on the detection of breast lesions were analyzed. Four types of breast lesions were simulated and inserted into clinically-acquired digital mammograms. Dose reduction by 50% and 75% of the original clinically-relevant exposure levels were simulated by adding corresponding simulated noise into the original mammograms. The mammograms were converted into luminance values corresponding to those displayed on a clinical soft-copy display station and subsequently analyzed by Laguerre-Gauss and Gabor channelized Hotelling observer models for differences in detectability performance with reduction in radiation dose. Performance was measured under amore » signal known exactly but variable detection task paradigm in terms of receiver operating characteristics (ROC) curves and area under the ROC curves. The results suggested that luminance mapping of digital mammograms affects performance of model observers. Reduction in dose levels by 50% lowered the detectability of masses with borderline statistical significance. Dose reduction did not have a statistically significant effect on detection of microcalcifications. The model results indicate that there is room for optimization of dose level in mammographic screening procedures.« less
  • Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less