6,367 research outputs found
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Good results on image classification and retrieval using support vector
machines (SVM) with local binary patterns (LBPs) as features have been
extensively reported in the literature where an entire image is retrieved or
classified. In contrast, in medical imaging, not all parts of the image may be
equally significant or relevant to the image retrieval application at hand. For
instance, in lung x-ray image, the lung region may contain a tumour, hence
being highly significant whereas the surrounding area does not contain
significant information from medical diagnosis perspective. In this paper, we
propose to detect salient regions of images during training and fold the data
to reduce the effect of irrelevant regions. As a result, smaller image areas
will be used for LBP features calculation and consequently classification by
SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the
performance of the proposed approach. The results demonstrate the benefits of
saliency-based folding approach that delivers comparable classification
accuracies with state-of-the-art but exhibits lower computational cost and
storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
201
Autoencoding the Retrieval Relevance of Medical Images
Content-based image retrieval (CBIR) of medical images is a crucial task that
can contribute to a more reliable diagnosis if applied to big data. Recent
advances in feature extraction and classification have enormously improved CBIR
results for digital images. However, considering the increasing accessibility
of big data in medical imaging, we are still in need of reducing both memory
requirements and computational expenses of image retrieval systems. This work
proposes to exclude the features of image blocks that exhibit a low encoding
error when learned by a autoencoder (). We examine the
histogram of autoendcoding errors of image blocks for each image class to
facilitate the decision which image regions, or roughly what percentage of an
image perhaps, shall be declared relevant for the retrieval task. This leads to
reduction of feature dimensionality and speeds up the retrieval process. To
validate the proposed scheme, we employ local binary patterns (LBP) and support
vector machines (SVM) which are both well-established approaches in CBIR
research community. As well, we use IRMA dataset with 14,410 x-ray images as
test data. The results show that the dimensionality of annotated feature
vectors can be reduced by up to 50% resulting in speedups greater than 27% at
expense of less than 1% decrease in the accuracy of retrieval when validating
the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image
Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015,
Orleans, Franc
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.
In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists
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