Abstract: This paper investigates successful sub-bands of wave atom transform via classification of mammograms, when the coefficients of sub-bands are used as features. A computer-aided diagnosis system is constructed by using wave atom transform, support vector machine and k-nearest neighbor classifiers. Two-class classification is studied in detail using two data sets, separately. The successful sub-bands are determined according to the accuracy rates, coefficient numbers, and sensitivity rates.
Abstract: One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).
Abstract: Cancer affects people globally with breast cancer being a leading killer. Breast cancer is due to the uncontrollable multiplication of cells resulting in a tumour or neoplasm. Tumours are called ‘benign’ when cancerous cells do not ravage other body tissues and ‘malignant’ if they do so. As mammography is an effective breast cancer detection tool at an early stage which is the most treatable stage it is the primary imaging modality for screening and diagnosis of this cancer type. This paper presents an automatic mammogram classification technique using wavelet and Gabor filter. Correlation feature selection is used to reduce the feature set and selected features are classified using different decision trees.
Abstract: Computer aided diagnosis systems provide vital
opinion to radiologists in the detection of early signs of breast cancer
from mammogram images. Architectural distortions, masses and
microcalcifications are the major abnormalities. In this paper, a
computer aided diagnosis system has been proposed for
distinguishing abnormal mammograms with architectural distortion
from normal mammogram. Four types of texture features GLCM
texture, GLRLM texture, fractal texture and spectral texture features
for the regions of suspicion are extracted. Support vector machine
has been used as classifier in this study. The proposed system yielded
an overall sensitivity of 96.47% and an accuracy of 96% for
mammogram images collected from digital database for screening
mammography database.
Abstract: Mammography is widely used technique for breast cancer
screening. There are various other techniques for breast cancer screening
but mammography is the most reliable and effective technique. The
images obtained through mammography are of low contrast which
causes problem for the radiologists to interpret. Hence, a high quality
image is mandatory for the processing of the image for extracting any
kind of information from it. Many contrast enhancement algorithms have
been developed over the years. In the present work, an efficient
morphology based technique is proposed for contrast enhancement of
masses in mammographic images. The proposed method is based on
Multiscale Morphology and it takes into consideration the scale of the
structuring element. The proposed method is compared with other stateof-
the-art techniques. The experimental results show that the proposed
method is better both qualitatively and quantitatively than the other
standard contrast enhancement techniques.
Abstract: Breast region segmentation is an essential prerequisite in computerized analysis of mammograms. It aims at separating the breast tissue from the background of the mammogram and it includes two independent segmentations. The first segments the background region which usually contains annotations, labels and frames from the whole breast region, while the second removes the pectoral muscle portion (present in Medio Lateral Oblique (MLO) views) from the rest of the breast tissue. In this paper we propose hybridization of Connected Component Labeling (CCL), Fuzzy, and Straight line methods. Our proposed methods worked good for separating pectoral region. After removal pectoral muscle from the mammogram, further processing is confined to the breast region alone. To demonstrate the validity of our segmentation algorithm, it is extensively tested using over 322 mammographic images from the Mammographic Image Analysis Society (MIAS) database. The segmentation results were evaluated using a Mean Absolute Error (MAE), Hausdroff Distance (HD), Probabilistic Rand Index (PRI), Local Consistency Error (LCE) and Tanimoto Coefficient (TC). The hybridization of fuzzy with straight line method is given more than 96% of the curve segmentations to be adequate or better. In addition a comparison with similar approaches from the state of the art has been given, obtaining slightly improved results. Experimental results demonstrate the effectiveness of the proposed approach.
Abstract: The paper aims to specify and build a system, a learning support in radiology-senology (breast radiology) dedicated to help assist junior radiologists-senologists in their radiologysenology- related activity based on experience of expert radiologistssenologists. This system is named SAFRS (i.e. system supporting the training of radiologists-senologists). It is based on the exploitation of radiologic-senologic images (primarily mammograms but also echographic images or MRI) and their related clinical files. The aim of such a system is to help breast cancer screening in education. In order to acquire this expert radiologist-senologist knowledge, we have used the CBR (case-based reasoning) approach. The SAFRS system will promote the evolution of teaching in radiology-senology by offering the “junior radiologist" trainees an advanced pedagogical product. It will permit a strengthening of knowledge together with a very elaborate presentation of results. At last, the know-how will derive from all these factors.
Abstract: This paper presents an approach for early breast
cancer diagnostic by employing combination of artificial neural
networks (ANN) and multiwaveletpacket based subband image
decomposition. The microcalcifications correspond to high-frequency
components of the image spectrum, detection of microcalcifications
is achieved by decomposing the mammograms into different
frequency subbands,, reconstructing the mammograms from the
subbands containing only high frequencies. For this approach we
employed different types of multiwaveletpacket. We used the result
as an input of neural network for classification. The proposed
methodology is tested using the Nijmegen and the Mammographic
Image Analysis Society (MIAS) mammographic databases and
images collected from local hospitals. Results are presented as the
receiver operating characteristic (ROC) performance and are
quantified by the area under the ROC curve.
Abstract: Clusters of microcalcifications in mammograms are an
important sign of breast cancer. This paper presents a complete
Computer Aided Detection (CAD) scheme for automatic detection of
clustered microcalcifications in digital mammograms. The proposed
system, MammoScan μCaD, consists of three main steps. Firstly
all potential microcalcifications are detected using a a method for
feature extraction, VarMet, and adaptive thresholding. This will also
give a number of false detections. The goal of the second step,
Classifier level 1, is to remove everything but microcalcifications.
The last step, Classifier level 2, uses learned dictionaries and sparse
representations as a texture classification technique to distinguish
single, benign microcalcifications from clustered microcalcifications,
in addition to remove some remaining false detections. The system
is trained and tested on true digital data from Stavanger University
Hospital, and the results are evaluated by radiologists. The overall
results are promising, with a sensitivity > 90 % and a low false
detection rate (approx 1 unwanted pr. image, or 0.3 false pr. image).
Abstract: Breast skin-line estimation and breast segmentation is an important pre-process in mammogram image processing and computer-aided diagnosis of breast cancer. Limiting the area to be processed into a specific target region in an image would increase the accuracy and efficiency of processing algorithms. In this paper we are presenting a new algorithm for estimating skin-line and breast segmentation using fast marching algorithm. Fast marching is a partial-differential equation based numerical technique to track evolution of interfaces. We have introduced some modifications to the traditional fast marching method, specifically to improve the accuracy of skin-line estimation and breast tissue segmentation. Proposed modifications ensure that the evolving front stops near the desired boundary. We have evaluated the performance of the algorithm by using 100 mammogram images taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that this algorithm explains 98.6% of the ground truth breast region and accuracy of the segmentation is 99.1%. Also this algorithm is capable of partially-extracting nipple when it is available in the profile.
Abstract: Texture classification is an important image processing
task with a broad application range. Many different techniques for
texture classification have been explored. Using sparse approximation
as a feature extraction method for texture classification is a relatively
new approach, and Skretting et al. recently presented the Frame
Texture Classification Method (FTCM), showing very good results on
classical texture images. As an extension of that work the FTCM is
here tested on a real world application as detection of abnormalities
in mammograms. Some extensions to the original FTCM that are
useful in some applications are implemented; two different smoothing
techniques and a vector augmentation technique. Both detection of
microcalcifications (as a primary detection technique and as a last
stage of a detection scheme), and soft tissue lesions in mammograms
are explored. All the results are interesting, and especially the results
using FTCM on regions of interest as the last stage in a detection
scheme for microcalcifications are promising.
Abstract: X-ray mammography is the most effective method for
the early detection of breast diseases. However, the typical diagnostic
signs such as microcalcifications and masses are difficult to detect
because mammograms are of low-contrast and noisy. In this paper, a
new algorithm for image denoising and enhancement in Orthogonal
Polynomials Transformation (OPT) is proposed for radiologists to
screen mammograms. In this method, a set of OPT edge coefficients
are scaled to a new set by a scale factor called OPT scale factor. The
new set of coefficients is then inverse transformed resulting in
contrast improved image. Applications of the proposed method to
mammograms with subtle lesions are shown. To validate the
effectiveness of the proposed method, we compare the results to
those obtained by the Histogram Equalization (HE) and the Unsharp
Masking (UM) methods. Our preliminary results strongly suggest
that the proposed method offers considerably improved enhancement
capability over the HE and UM methods.
Abstract: An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
Abstract: Mammography is the most effective procedure for an
early diagnosis of the breast cancer. Nowadays, people are trying to
find a way or method to support as much as possible to the
radiologists in diagnosis process. The most popular way is now being
developed is using Computer-Aided Detection (CAD) system to
process the digital mammograms and prompt the suspicious region to
radiologist. In this paper, an automated CAD system for detection
and classification of massive lesions in mammographic images is
presented. The system consists of three processing steps: Regions-Of-
Interest detection, feature extraction and classification. Our CAD
system was evaluated on Mini-MIAS database consisting 322
digitalized mammograms. The CAD system-s performance is
evaluated using Receiver Operating Characteristics (ROC) and Freeresponse
ROC (FROC) curves. The archived results are 3.47 false
positives per image (FPpI) and sensitivity of 85%.
Abstract: Two algorithms are proposed to reduce the storage requirements for mammogram images. The input image goes through a shrinking process that converts the 16-bit images to 8-bits by using pixel-depth conversion algorithm followed by enhancement process. The performance of the algorithms is evaluated objectively and subjectively. A 50% reduction in size is obtained with no loss of significant data at the breast region.
Abstract: Breast cancer detection techniques have been reported
to aid radiologists in analyzing mammograms. We note that most
techniques are performed on uncompressed digital mammograms.
Mammogram images are huge in size necessitating the use of
compression to reduce storage/transmission requirements. In this
paper, we present an algorithm for the detection of
microcalcifications in the JPEG2000 domain. The algorithm is based
on the statistical properties of the wavelet transform that the
JPEG2000 coder employs. Simulation results were carried out at
different compression ratios. The sensitivity of this algorithm ranges
from 92% with a false positive rate of 4.7 down to 66% with a false
positive rate of 2.1 using lossless compression and lossy compression
at a compression ratio of 100:1, respectively.
Abstract: This paper presents a novel approach to finding a
priori interesting regions in mammograms. In order to delineate those
regions of interest (ROI-s) in mammograms, which appear to be
prominent, a topographic representation called the iso-level contour
map consisting of iso-level contours at multiple intensity levels and
region segmentation based-thresholding have been proposed. The
simulation results indicate that the computed boundary gives the
detection rate of 99.5% accuracy.
Abstract: Medical imaging uses the advantage of digital
technology in imaging and teleradiology. In teleradiology systems
large amount of data is acquired, stored and transmitted. A major
technology that may help to solve the problems associated with the
massive data storage and data transfer capacity is data compression
and decompression. There are many methods of image compression
available. They are classified as lossless and lossy compression
methods. In lossy compression method the decompressed image
contains some distortion. Fractal image compression (FIC) is a lossy
compression method. In fractal image compression an image is
coded as a set of contractive transformations in a complete metric
space. The set of contractive transformations is guaranteed to
produce an approximation to the original image. In this paper FIC is
achieved by PIFS using quadtree partitioning. PIFS is applied on
different images like , Ultrasound, CT Scan, Angiogram, X-ray,
Mammograms. In each modality approximately twenty images are
considered and the average values of compression ratio and PSNR
values are arrived. In this method of fractal encoding, the
parameter, tolerance factor Tmax, is varied from 1 to 10, keeping the
other standard parameters constant. For all modalities of images the
compression ratio and Peak Signal to Noise Ratio (PSNR) are
computed and studied. The quality of the decompressed image is
arrived by PSNR values. From the results it is observed that the
compression ratio increases with the tolerance factor and
mammogram has the highest compression ratio. The quality of the
image is not degraded upto an optimum value of tolerance factor,
Tmax, equal to 8, because of the properties of fractal compression.
Abstract: Accurate assessment of the primary tumor response to
treatment is important in the management of breast cancer. This
paper introduces a new set of treatment evaluation indicators for
breast cancer cases based on the computational process of three
known metrics, the Euclidian, Hamming and Levenshtein distances.
The distance principals are applied to pairs of mammograms and/or
echograms, recorded before and after treatment, determining a
reference point in judging the evolution amount of the studied
carcinoma. The obtained numerical results are indeed very
transparent and indicate not only the evolution or the involution of
the tumor under treatment, but also a quantitative measurement of the
benefit in using the selected method of treatment.