8,260 research outputs found
Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology
Accurate detection of mitosis plays a critical role in breast cancer
histopathology. Manual detection and counting of mitosis is tedious and subject
to considerable inter- and intra-reader variations. Multispectral imaging is a
recent medical imaging technology, proven successful in increasing the
segmentation accuracy in other fields. This study aims at improving the
accuracy of mitosis detection by developing a specific solution using
multispectral and multifocal imaging of breast cancer histopathological data.
We propose to enable clinical routine-compliant quality of mitosis
discrimination from other objects. The proposed framework includes
comprehensive analysis of spectral bands and z-stack focus planes, detection of
expected mitotic regions (candidates) in selected focus planes and spectral
bands, computation of multispectral spatial features for each candidate,
selection of multispectral spatial features and a study of different
state-of-the-art classification methods for candidates classification as
mitotic or non mitotic figures. This framework has been evaluated on MITOS
multispectral medical dataset and achieved 60% detection rate and 57%
F-Measure. Our results indicate that multispectral spatial features have more
information for mitosis classification in comparison with white spectral band
features, being therefore a very promising exploration area to improve the
quality of the diagnosis assistance in histopathology
Tuning for Tissue Image Segmentation Workflows for Accuracy and Performance
We propose a software platform that integrates methods and tools for
multi-objective parameter auto- tuning in tissue image segmentation workflows.
The goal of our work is to provide an approach for improving the accuracy of
nucleus/cell segmentation pipelines by tuning their input parameters. The
shape, size and texture features of nuclei in tissue are important biomarkers
for disease prognosis, and accurate computation of these features depends on
accurate delineation of boundaries of nuclei. Input parameters in many nucleus
segmentation workflows affect segmentation accuracy and have to be tuned for
optimal performance. This is a time-consuming and computationally expensive
process; automating this step facilitates more robust image segmentation
workflows and enables more efficient application of image analysis in large
image datasets. Our software platform adjusts the parameters of a nuclear
segmentation algorithm to maximize the quality of image segmentation results
while minimizing the execution time. It implements several optimization methods
to search the parameter space efficiently. In addition, the methodology is
developed to execute on high performance computing systems to reduce the
execution time of the parameter tuning phase. Our results using three
real-world image segmentation workflows demonstrate that the proposed solution
is able to (1) search a small fraction (about 100 points) of the parameter
space, which contains billions to trillions of points, and improve the quality
of segmentation output by 1.20x, 1.29x, and 1.29x, on average; (2) decrease the
execution time of a segmentation workflow by up to 11.79x while improving
output quality; and (3) effectively use parallel systems to accelerate
parameter tuning and segmentation phases.Comment: 29 pages, 5 figure
Image Processing on IOPA Radiographs: A comprehensive case study on Apical Periodontitis
With the recent advancements in Image Processing Techniques and development
of new robust computer vision algorithms, new areas of research within Medical
Diagnosis and Biomedical Engineering are picking up pace. This paper provides a
comprehensive in-depth case study of Image Processing, Feature Extraction and
Analysis of Apical Periodontitis diagnostic cases in IOPA (Intra Oral
Peri-Apical) Radiographs, a common case in oral diagnostic pipeline. This paper
provides a detailed analytical approach towards improving the diagnostic
procedure with improved and faster results with higher accuracy targeting to
eliminate True Negative and False Positive cases.Comment: 15 pages, 42 figures and Submitted at ICIAP 2019: 21st International
Conference on Image Analysis and Processin
Detection and classification of masses in mammographic images in a multi-kernel approach
According to the World Health Organization, breast cancer is the main cause
of cancer death among adult women in the world. Although breast cancer occurs
indiscriminately in countries with several degrees of social and economic
development, among developing and underdevelopment countries mortality rates
are still high, due to low availability of early detection technologies. From
the clinical point of view, mammography is still the most effective diagnostic
technology, given the wide diffusion of the use and interpretation of these
images. Herein this work we propose a method to detect and classify
mammographic lesions using the regions of interest of images. Our proposal
consists in decomposing each image using multi-resolution wavelets. Zernike
moments are extracted from each wavelet component. Using this approach we can
combine both texture and shape features, which can be applied both to the
detection and classification of mammary lesions. We used 355 images of fatty
breast tissue of IRMA database, with 233 normal instances (no lesion), 72
benign, and 83 malignant cases. Classification was performed by using SVM and
ELM networks with modified kernels, in order to optimize accuracy rates,
reaching 94.11%. Considering both accuracy rates and training times, we defined
the ration between average percentage accuracy and average training time in a
reverse order. Our proposal was 50 times higher than the ratio obtained using
the best method of the state-of-the-art. As our proposed model can combine high
accuracy rate with low learning time, whenever a new data is received, our work
will be able to save a lot of time, hours, in learning process in relation to
the best method of the state-of-the-art
Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks
Fine-grained classification of cervical cells into different abnormality
levels is of great clinical importance but remains very challenging. Contrary
to traditional classification methods that rely on hand-crafted or engineered
features, convolution neural network (CNN) can classify cervical cells based on
automatically learned deep features. However, CNN in previous studies do not
involve cell morphological information, and it is unknown whether morphological
features can be directly modeled by CNN to classify cervical cells. This paper
presents a CNN-based method that combines cell image appearance with cell
morphology for classification of cervical cells in Pap smear. The training
cervical cell dataset consists of adaptively re-sampled image patches coarsely
centered on the nuclei. Several CNN models (AlexNet, GoogleNet, ResNet and
DenseNet) pre-trained on ImageNet dataset were fine-tuned on the cervical
dataset for comparison. The proposed method is evaluated on the Herlev cervical
dataset by five-fold cross-validation at patient level splitting. Results show
that by adding cytoplasm and nucleus masks as raw morphological information
into appearance-based CNN learning, higher classification accuracies can be
achieved in general. Among the four CNN models, GoogleNet fed with both
morphological and appearance information obtains the highest classification
accuracies of 94.5% for 2-class classification task and 64.5% for 7-class
classification task. Our method demonstrates that combining cervical cell
morphology with appearance information can provide improved classification
performance, which is clinically important for early diagnosis of cervical
dysplastic changes.Comment: 7 pages, 4 figure
Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy
This paper attempts to provide the reader a place to begin studying the
application of computer vision and machine learning to gastrointestinal (GI)
endoscopy. They have been classified into 18 categories. It should be be noted
by the reader that this is a review from pre-deep learning era. A lot of deep
learning based applications have not been covered in this thesis
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
The visual attributes of cells, such as the nuclear morphology and chromatin
openness, are critical for histopathology image analysis. By learning
cell-level visual representation, we can obtain a rich mix of features that are
highly reusable for various tasks, such as cell-level classification, nuclei
segmentation, and cell counting. In this paper, we propose a unified generative
adversarial networks architecture with a new formulation of loss to perform
robust cell-level visual representation learning in an unsupervised setting.
Our model is not only label-free and easily trained but also capable of
cell-level unsupervised classification with interpretable visualization, which
achieves promising results in the unsupervised classification of bone marrow
cellular components. Based on the proposed cell-level visual representation
learning, we further develop a pipeline that exploits the varieties of cellular
elements to perform histopathology image classification, the advantages of
which are demonstrated on bone marrow datasets.Comment: Accepted for publication in IEEE Journal of Biomedical and Health
Informatic
A Complete System for Candidate Polyps Detection in Virtual Colonoscopy
Computer tomographic colonography, combined with computer-aided detection, is
a promising emerging technique for colonic polyp analysis. We present a
complete pipeline for polyp detection, starting with a simple colon
segmentation technique that enhances polyps, followed by an adaptive-scale
candidate polyp delineation and classification based on new texture and
geometric features that consider both the information in the candidate polyp
location and its immediate surrounding area. The proposed system is tested with
ground truth data, including flat and small polyps which are hard to detect
even with optical colonoscopy. For polyps larger than 6mm in size we achieve
100% sensitivity with just 0.9 false positives per case, and for polyps larger
than 3mm in size we achieve 93% sensitivity with 2.8 false positives per case
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis
Classification is one of the core problems in Computer-Aided Diagnosis (CAD),
targeting for early cancer detection using 3D medical imaging interpretation.
High detection sensitivity with desirably low false positive (FP) rate is
critical for a CAD system to be accepted as a valuable or even indispensable
tool in radiologists' workflow. Given various spurious imagery noises which
cause observation uncertainties, this remains a very challenging task. In this
paper, we propose a novel, two-tiered coarse-to-fine (CTF) classification
cascade framework to tackle this problem. We first obtain
classification-critical data samples (e.g., samples on the decision boundary)
extracted from the holistic data distributions using a robust parametric model
(e.g., \cite{Raykar08}); then we build a graph-embedding based nonparametric
classifier on sampled data, which can more accurately preserve or formulate the
complex classification boundary. These two steps can also be considered as
effective "sample pruning" and "feature pursuing + NN/template matching",
respectively. Our approach is validated comprehensively in colorectal polyp
detection and lung nodule detection CAD systems, as the top two deadly cancers,
using hospital scale, multi-site clinical datasets. The results show that our
method achieves overall better classification/detection performance than
existing state-of-the-art algorithms using single-layer classifiers, such as
the support vector machine variants \cite{Wang08}, boosting \cite{Slabaugh10},
logistic regression \cite{Ravesteijn10}, relevance vector machine
\cite{Raykar08}, -nearest neighbor \cite{Murphy09} or spectral projections
on graph \cite{Cai08}
3D Contouring for Breast Tumor in Sonography
Malignant and benign breast tumors present differently in their shape and
size on sonography. Morphological information provided by tumor contours are
important in clinical diagnosis. However, ultrasound images contain noises and
tissue texture; clinical diagnosis thus highly depends on the experience of
physicians. The manual way to sketch three-dimensional (3D) contours of breast
tumor is a time-consuming and complicate task. If automatic contouring could
provide a precise breast tumor contour that might assist physicians in making
an accurate diagnosis. This study presents an efficient method for
automatically contouring breast tumors in 3D sonography. The proposed method
utilizes an efficient segmentation procedure, i.e. level-set method (LSM), to
automatic detect contours of breast tumors. This study evaluates 20 cases
comprising ten benign and ten malignant tumors. The results of computer
simulation reveal that the proposed 3D segmentation method provides robust
contouring for breast tumor on ultrasound images. This approach consistently
obtains contours similar to those obtained by manual contouring of the breast
tumor and can save much of the time required to sketch precise contours.Comment: 18 pages, 1 table and 5 figure
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