556 research outputs found
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.Comment: 6 page
An Unsupervised Method for Suspicious Regions Detection in Mammogram Images
Over the past years many researchers proposed biomedical imaging methods for computer-aided detection
and classification of suspicious regions in mammograms. Mammogram interpretation is performed by
radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings
labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method
to automatically detect suspicious regions in mammogram images. The method consists mainly of two
steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background
region from the breast profile region. In greater detail, gray levels mapping transform and histogram
specifications are used to enhance the visual representation of mammogram details. Then, local keypoints
and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are
filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and
confidence interval are very encouraging
A comparative evaluation of two algorithms of detection of masses on mammograms
In this paper, we implement and carry out the comparison of two methods of
computer-aided-detection of masses on mammograms. The two algorithms basically
consist of 3 steps each: segmentation, binarization and noise suppression using
different techniques for each step. A database of 60 images was used to compare
the performance of the two algorithms in terms of general detection efficiency,
conservation of size and shape of detected masses.Comment: 9 pages, 5 figures, 1 table, Vol.3, No.1, February 2012,pp19-27;
Signal & Image Processing : An International Journal (SIPIJ),201
Texture descriptors applied to digital mammography
Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.Postprint (published version
Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities
Perceiving abnormal regions in the images of different medical modalities plays a crucial role in diagnosis
and subsequent treatment planning. In medical images to visually perceive abnormalities’ extent and
boundaries requires substantial experience. Consequently, manually drawn region of interest (ROI) to
outline boundaries of abnormalities suffers from limitations of human perception leading to inter-observer
variability. As an alternative to human drawn ROI, it is proposed the use of a computer-based segmenta-
tion algorithm to segment digital medical image data.
Hierarchical Clustering-based Segmentation (HCS) process is a generic unsupervised segmentation
process that can be used to segment dissimilar regions in digital images. HCS process generates a hierarchy
of segmented images by partitioning an image into its constituent regions at hierarchical levels of allowable
dissimilarity between its different regions. The hierarchy represents the continuous merging of similar,
spatially adjacent, and/or disjoint regions as the allowable threshold value of dissimilarity between regions,
for merging, is gradually increased.
This chapter discusses in detail first the implementation of the HCS process, second the implementa-
tion details of how the HCS process is used for the presentation of multi-modal imaging data (MALDI and
MRI) of a biological sample, third the implementation details of how the process is used as a perception
aid for X-ray mammogram readers, and finally the implementation details of how it is used as an interpreta-
tion aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with
arXiv:1612.0597
Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits
This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed.
Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach.
Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms.
Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature.
Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound
Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities
Perceiving abnormal regions in the images of different medical modalities plays a crucial role in diagnosis
and subsequent treatment planning. In medical images to visually perceive abnormalities’ extent and
boundaries requires substantial experience. Consequently, manually drawn region of interest (ROI) to
outline boundaries of abnormalities suffers from limitations of human perception leading to inter-observer
variability. As an alternative to human drawn ROI, it is proposed the use of a computer-based segmenta-
tion algorithm to segment digital medical image data.
Hierarchical Clustering-based Segmentation (HCS) process is a generic unsupervised segmentation
process that can be used to segment dissimilar regions in digital images. HCS process generates a hierarchy
of segmented images by partitioning an image into its constituent regions at hierarchical levels of allowable
dissimilarity between its different regions. The hierarchy represents the continuous merging of similar,
spatially adjacent, and/or disjoint regions as the allowable threshold value of dissimilarity between regions,
for merging, is gradually increased.
This chapter discusses in detail first the implementation of the HCS process, second the implementa-
tion details of how the HCS process is used for the presentation of multi-modal imaging data (MALDI and
MRI) of a biological sample, third the implementation details of how the process is used as a perception
aid for X-ray mammogram readers, and finally the implementation details of how it is used as an interpreta-
tion aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate
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