6 research outputs found
A new area-based convexity measure with distance weighted area integration for planar shapes
In this paper we propose a new area-based convexity measure. We assume that convexity evaluation of an arbitrary planar shape is related to the total influence of dents of the shape, and discover that those attributes of the dents, such as the position, area, and depth with respect to the Geometric Center of Convex Hull (GCCH) of the shape, determine the dent influence. We consider that the convex hull of the shape consists of infinitely small patches, to each of which we assign a weight showing the patch influence. We can simply integrate all the patch weights in any regions within the convex hull to calculate their total influence. We define this operation as the Distance Weighted Area Integration, if the weight is associated with the Euclidean distance from the patch to the GCCH. Our new measure is a distance weighted generalization of the most commonly used convexity measure, making this conventional measure fully replaceable for the first time. Experiments demonstrate advantages of the new convexity measure against the existing ones
A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images
Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively
Automatic BIRAD scoring of breast cancer mammograms
A computer aided diagnosis system (CAD) is developed to fully characterize and
classify mass to benign and malignancy and to predict BIRAD (Breast Imaging
Reporting and Data system) scores using mammographic image data. The CAD
includes a preprocessing step to de-noise mammograms. This is followed by an
active counter segmentation to deforms an initial curve, annotated by a
radiologist, to separate and define the boundary of a mass from background. A
feature extraction scheme wasthen used to fully characterize a mass by extraction
of the most relevant features that have a large impact on the outcome of a patient
biopsy. For this thirty-five medical and mathematical features based on intensity,
shape and texture associated to the mass were extracted. Several feature selection
schemes were then applied to select the most dominant features for use in next
step, classification. Finally, a hierarchical classification schemes were applied on
those subset of features to firstly classify mass to benign (mass with BIRAD score
2) and malignant mass (mass with BIRAD score over 4), and secondly to sub classify
mass with BIRAD score over 4 to three classes (BIRAD with score 4a,4b,4c).
Accuracy of segmentation performance were evaluated by calculating the degree
of overlapping between the active counter segmentation and the manual
segmentation, and the result was 98.5%. Also reproducibility of active counter
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using different manual initialization of algorithm by three radiologists were
assessed and result was 99.5%.
Classification performance was evaluated using one hundred sixty masses (80
masses with BRAD score 2 and 80 mass with BIRAD score over4). The best result
for classification of data to benign and malignance was found using a combination
of sequential forward floating feature (SFFS) selection and a boosted tree hybrid
classifier with Ada boost ensemble method, decision tree learner type and 100
learners’ regression tree classifier, achieving 100% sensitivity and specificity in
hold out method, 99.4% in cross validation method and 98.62 % average accuracy
in cross validation method.
For further sub classification of eighty malignance data with BIRAD score of over
4 (30 mass with BIRAD score 4a,30 masses with BIRAD score 4b and 20 masses with
BIRAD score 4c), the best result achieved using the boosted tree with ensemble
method bag, decision tree learner type with 200 learners Classification, achieving
100% sensitivity and specificity in hold out method, 98.8% accuracy and 98.41%
average accuracy for ten times run in cross validation method.
Beside those 160 masses (BIRAD score 2 and over 4) 13 masses with BIRAD score
3 were gathered. Which means patient is recommended to be tested in another
medical imaging technique and also is recommended to do follow-up in six
months. The CAD system was trained with mass with BIRAD score 2 and over 4 also
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it was further tested using 13 masses with a BIRAD score of 3 and the CAD results
are shown to agree with the radiologist’s classification after confirming in six
months follow up.
The present results demonstrate high sensitivity and specificity of the proposed
CAD system compared to prior research. The present research is therefore
intended to make contributions to the field by proposing a novel CAD system,
consists of series of well-selected image processing algorithms, to firstly classify
mass to benign or malignancy, secondly sub classify BIRAD 4 to three groups and
finally to interpret BIRAD 3 to BIRAD 2 without a need of follow up study
Classification of pathological shapes using convexity measures
Two new shape measures for quantifying the degree of convexity are described. When applied to assessment of skin lesions they are shown to be an effective indicator of malignancy, outperforming Lee et al’s. OII scale–space based irregularity measure. In addition, the new measures were applied to the classification of mammographic masses and lung field boundaries and were shown to perform well relative to a large set of common shape measures that appear in the literature such as moments, compactness, symmetry, etc