61 research outputs found
Detection of microcalcifications in mammograms using error of prediction and statistical measures
A two-stage method for detecting microcalcifications in
mammograms is presented. In the first stage, the determination of
the candidates for microcalcifications is performed. For this purpose,
a 2-D linear prediction error filter is applied, and for those pixels
where the prediction error is larger than a threshold, a statistical
measure is calculated to determine whether they are candidates for
microcalcifications or not. In the second stage, a feature vector is
derived for each candidate, and after a classification step using a
support vector machine, the final detection is performed. The algorithm
is tested with 40 mammographic images, from Screen Test:
The Alberta Program for the Early Detection of Breast Cancer with
50- m resolution, and the results are evaluated using a freeresponse
receiver operating characteristics curve. Two different
analyses are performed: an individual microcalcification detection
analysis and a cluster analysis. In the analysis of individual microcalcifications,
detection sensitivity values of 0.75 and 0.81 are obtained
at 2.6 and 6.2 false positives per image, on the average,
respectively. The best performance is characterized by a sensitivity
of 0.89, a specificity of 0.99, and a positive predictive value of 0.79.
In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77
false positives per image, and a value of 0.90 is achieved at 0.94
false positive per imag
Segmentation-based lossless compression of burn wound images
Color images may be encoded by using a gray-scale image
compression technique on each of the three color planes. Such
an approach, however, does not take advantage of the correlation
existing between the color planes. In this paper, a new
segmentation-based lossless compression method is proposed for
color images. The method exploits the correlation existing among
the three color planes by treating each pixel as a vector of three
components, performing region growing and difference operations
using the vectors, and applying a color coordinate transformation.
The method performed better than the Joint Photographic Experts
Group (JPEG) standard by an average of 3.40 bits/pixel with a database
including four natural color images of scenery, four images of
burn wounds, and four fractal images, and it outperformed the Joint
Bi-Level Image experts Group (JBIG) standard by an average of
3.01 bits/pixel. When applied to a database of 20 burn wound images,
the 24 bits/pixel images were efficiently compressed to 4.79
bits/pixel, then requiring 4.16 bits/pixel less than JPEG and 5.41
bits/pixel less than JBIG
Detection of microcalcifications in mammograms using error of prediction and statistical measures
A two-stage method for detecting microcalcifications in
mammograms is presented. In the first stage, the determination of
the candidates for microcalcifications is performed. For this purpose,
a 2-D linear prediction error filter is applied, and for those pixels
where the prediction error is larger than a threshold, a statistical
measure is calculated to determine whether they are candidates for
microcalcifications or not. In the second stage, a feature vector is
derived for each candidate, and after a classification step using a
support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test:
The Alberta Program for the Early Detection of Breast Cancer with
50- m resolution, and the results are evaluated using a freeresponse receiver operating characteristics curve. Two different
analyses are performed: an individual microcalcification detection
analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average,
respectively. The best performance is characterized by a sensitivity
of 0.89, a specificity of 0.99, and a positive predictive value of 0.79.
In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77
false positives per image, and a value of 0.90 is achieved at 0.94
false positive per imageMinisterio de Sanidad FIS05-202
Characterization and pattern recognition of color images of dermatological ulcers: a pilot study
We present color image processing methods for the char\-ac\-te\-ri\-za\-tion of images of dermatological lesions for the purpose of content-based image retrieval (CBIR) and computer-aided di\-ag\-no\-sis. The intended application is to segment the images and perform classification and analysis of the tissue composition of skin lesions or ulcers, in terms of granulation (red), fibrin (yel\-low), necrotic (black), callous (white), and mixed tissue composition. The images were analyzed and classified by an expert dermatologist following the red-yellow-black-white model. Automatic segmentation was performed by means of clustering using Gauss\-ian mixture modeling, and its performance was evaluated by deriving the Jaccard coefficient between the automatically and manually segmented images. Statistical texture features were derived from cooccurrence matrices of RGB, HSI, Lab, and Luv color components. A retrieval engine was implemented using the k-nearest-neighbor classifier and the Euclidean, Man\-hat\-tan, and Chebyshev distance metrics. Classification was performed by means of a metaclassifier using logistic regression. The average Jaccard coefficient after the segmentation step between the automatically and manually segmented images was 0.560, with a standard deviation of 0.220. The performance in CBIR was mea\-sured in terms of precision of retrieval, with average values of up to 0.617 obtained with the Chebyshev distance. The metaclassifier yielded an average area under the receiver operating char\-ac\-ter\-is\-tic curve of 0.772
Biomedical signal analysis
The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. Wide range of filtering techniques presented to address various applications. 800 mathematical expressions and equations. Practical questions, problems and laboratory exercises. Includes fractals and chaos theory with biomedical applications
Correction to “Detection of architectural distortion in prior mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis [1]”
Subjective and objective evaluation of image sharpness: behavior of the region-based image edge profile acutance measure
We recently proposed a region-based measure of image edge profile acutance to characterize the sharpness of a region of interest. In this paper we study the capability of the acutance measure to analyze relative sharpness in the presence of blurring and noise by comparing acutance to other measures of distortion and to subjective evaluation. The purpose of the experiment was to organize an image set in increasing order of sharpness with results obtained by objective image quality measures (acutance, mean squared error, normalized error, and normalized mean squared error) and to compare the results with subjective evaluation. A psychometric experiment was developed to perform sorting according to the subjective notion of sharpness. The region-based image edge profile acutance measure provided results that agree more closely with subjective evaluation of relative sharpness than the other measures studied. The acutance measure also exhibited a good level of immunity to noise, whereas the other measures provided ordering according to noise rather than sharpness
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