3,526 research outputs found
Gabor Barcodes for Medical Image Retrieval
In recent years, advances in medical imaging have led to the emergence of
massive databases, containing images from a diverse range of modalities. This
has significantly heightened the need for automated annotation of the images on
one side, and fast and memory-efficient content-based image retrieval systems
on the other side. Binary descriptors have recently gained more attention as a
potential vehicle to achieve these goals. One of the recently introduced binary
descriptors for tagging of medical images are Radon barcodes (RBCs) that are
driven from Radon transform via local thresholding. Gabor transform is also a
powerful transform to extract texture-based information. Gabor features have
exhibited robustness against rotation, scale, and also photometric
disturbances, such as illumination changes and image noise in many
applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework
for the image annotation. To find the most discriminative GBC for a given query
image, the effects of employing Gabor filters with different parameters, i.e.,
different sets of scales and orientations, are investigated, resulting in
different barcode lengths and retrieval performances. The proposed method has
been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray
images for indexing, and 1,733 x-rays images for testing. A total error score
as low as ( accuracy for the first hit) was achieved.Comment: To appear in proceedings of The 2016 IEEE International Conference on
Image Processing (ICIP 2016), Sep 25-28, 2016, Phoenix, Arizona, US
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition
Convolutional neural networks are sensitive to unknown noisy condition in the
test phase and so their performance degrades for the noisy data classification
task including noisy speech recognition. In this research, a new convolutional
neural network (CNN) model with data uncertainty handling; referred as NCNN
(Neutrosophic Convolutional Neural Network); is proposed for classification
task. Here, speech signals are used as input data and their noise is modeled as
uncertainty. In this task, using speech spectrogram, a definition of
uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed
for each Time-frequency point of speech spectrogram as like a pixel. Therefore,
uncertainty matrix with the same size of spectrogram is created in NS domain.
In the next step, a two parallel paths CNN classification model is proposed.
Speech spectrogram is used as input of the first path and uncertainty matrix
for the second path. The outputs of two paths are combined to compute the final
output of the classifier. To show the effectiveness of the proposed method, it
has been compared with conventional CNN on the isolated words of Aurora2
dataset. The proposed method achieves the average accuracy of 85.96 in noisy
train data. It is more robust against Car, Airport and Subway noises with
accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show
that the proposed method outperforms conventional CNN with the improvement of
6, 5 and 2 percentage in test set A, test set B and test sets C, respectively.
It means that the proposed method is more robust against noisy data and handle
these data effectively.Comment: International conference on Pattern Recognition and Image Analysis
(IPRIA 2019
Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
This paper proposes to generate and to use barcodes to annotate medical
images and/or their regions of interest such as organs, tumors and tissue
types. A multitude of efficient feature-based image retrieval methods already
exist that can assign a query image to a certain image class. Visual
annotations may help to increase the retrieval accuracy if combined with
existing feature-based classification paradigms. Whereas with annotations we
usually mean textual descriptions, in this paper barcode annotations are
proposed. In particular, Radon barcodes (RBC) are introduced. As well, local
binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as
barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test
images is used to verify how barcodes could facilitate image retrieval.Comment: To be published in proceedings of The IEEE International Conference
on Image Processing (ICIP 2015), September 27-30, 2015, Quebec City, Canad
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