7 research outputs found
Mammography Image Enhancement using Linear, Nonlinear and Wavelet Filters with Histogram Equalization
In the worldwide, breast cancer is one of the major diseases among the women. In the modern medical science, there are plenty of newly devised methodologies and techniques for the timely detection of breast cancer. However, there are difficulties still exist for detecting breast cancer at an early stage for its diagnoses because of poor visualization and artifacts present in the mammography. Thus the Digital mammographic image preprocessing often requires, enhancement of the image to improve the quality while preserving important details. The proposed method works in three stages. First it removes all the artifacts present in the image. Second it denoise the image by using Linear, nonlinear and wavelet filters. Third, contrast of the image increased by histogram equalization. This method definitely helps to computer aided diagnosis system to increase the accuracy. The experimental results are tested on two standard datasets MIAS and DDSM.
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Multiscale sub-octave wavelet transform for de-noising and enhancement
This paper describes an approach for accomplishing sub- octave wavelet analysis and its discrete implementation for noise reduction and feature enhancement. Sub-octave wavelet transforms allow us to more closely characterize features within distinct frequency bands. By dividing each octave into sub-octave components, we demonstrate a superior ability to capture transient activities in a signal or image more reliably. De-noising and enhancement are accomplished through techniques of minimizing noise energy and nonlinear processing of transform coefficient energy by gain
Comparison of denoising methods for digital mammographic image
We compared effects of denoising methods on digital mammographic images. The denoising methods studied were an adaptive Wiener filter and low–pass Gaussian filter. The denoising methods were applied as an image preprocessing techniques before enhancement. The performance of image denoising methods are based on Mean Squared Error (MSE) and Peak Signal To Ratio (PSNR) values
Mammography
In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume
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