1,149 research outputs found
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans
In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM)
Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection
Breast cancer is the most prevalent cancer that affects women all over the world. Early detection
and treatment of breast cancer could decline the mortality rate. Some issues such as technical
reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer
by radiologists. Computer-aided detection systems (CADs) are developed to overcome these
restrictions and have been studied in many imaging modalities for breast cancer detection in recent
years. The CAD systems improve radiologists’ performance in finding and discriminat- ing between
the normal and abnormal tissues. These procedures are performed only as a double reader but the
absolute decisions are still made by the radiologist. In this study, the recent CAD systems for
breast cancer detec- tion on different modalities such as mammography, ultrasound, MRI, and biopsy
histopathological images are introduced. The foundation of CAD systems generally consist of four
stages: Pre-processing, Segmentation, Fea- ture extraction, and Classification. The approaches
which applied to design different stages of CAD system are summarised. Advantages and disadvantages
of different segmentation, feature extraction and classification tech- niques are listed.
In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to
solve these issues are discussed. As well as, performance evaluation metrics for various stages of
breast cancer detection CAD systems are reviewed
Salient Objects in Clutter
This paper identifies and addresses a serious design bias of existing salient
object detection (SOD) datasets, which unrealistically assume that each image
should contain at least one clear and uncluttered salient object. This design
bias has led to a saturation in performance for state-of-the-art SOD models
when evaluated on existing datasets. However, these models are still far from
satisfactory when applied to real-world scenes. Based on our analyses, we
propose a new high-quality dataset and update the previous saliency benchmark.
Specifically, our dataset, called Salient Objects in Clutter~\textbf{(SOC)},
includes images with both salient and non-salient objects from several common
object categories. In addition to object category annotations, each salient
image is accompanied by attributes that reflect common challenges in common
scenes, which can help provide deeper insight into the SOD problem. Further,
with a given saliency encoder, e.g., the backbone network, existing saliency
models are designed to achieve mapping from the training image set to the
training ground-truth set. We, therefore, argue that improving the dataset can
yield higher performance gains than focusing only on the decoder design. With
this in mind, we investigate several dataset-enhancement strategies, including
label smoothing to implicitly emphasize salient boundaries, random image
augmentation to adapt saliency models to various scenarios, and self-supervised
learning as a regularization strategy to learn from small datasets. Our
extensive results demonstrate the effectiveness of these tricks. We also
provide a comprehensive benchmark for SOD, which can be found in our
repository: https://github.com/DengPingFan/SODBenchmark.Comment: 349 references, 20 pages, survey 201 models, benchmark 100 models.
Online benchmark: https://github.com/DengPingFan/SODBenchmar
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