Evaluating wavelet compression in microcalcification detection in mammography

Breast cancer is one of the most common cancer among women and the survival rate tends to be low when its stage found high when treated. To improve breast cancer survival, early detection is critical. There are two ways of detection for breast cancer: early diagnosis and screening. To make an accurate diagnosis in the early stage of breast cancer, the appearance of masses and micro-calcifications on the mammography image are two important indicators. It is time-consuming and challenging to identify micro-calcification from mammogram images by the human eye because of its size and appearance. Several Computer-Aided Detection (CADe) have been developed to support radiologists because the automatic detection of micro-calcification is important for diagnosis and the next recommended treatment. Most of the current CADe systems at this time started using Convolutional Neural Network (CNN) to implement the micro-calcification detection in mammograms and their quantitative results are very satisfying, the average level of accuracy is more than 90%. However, most of the methods used are image fragments from a complete image which are then included in the program. This research conducts an automated approach to detect the location of any micro-calcification in the mammogram images with the complete image and in a simple way. At first, the image preprocessing algorithms were applied to enhance the image quality. After that, the micro-calcification region was labeled using image segmentation based on the Radiologist's expertise. The positive label which contains micro-calcification pixels was taken to train with segmentation network. A total of 354 images from INbreast dataset were used in this research study. Finally, the trained network was utilized to detect the micro-calcification area automatically from the mammogram images. This process can help as an assistant to the radiologist for early diagnosis and increase the detection accuracy of the micro-calcification regions. The proposed system performance is measured according to the error values of Mean Squared Logarithmic Error (MSLE) as the technique to find out the difference between the values predicted by the proposed model and the actual values. The best MSLE loss value obtained was achieved in 0.05 with accuracy 0.95.

Cancer is typified by the hysterical explosion of peculiar cells which violates standard regulations of cell division. If proliferation persists and disseminates, resulting in the creation of metastases, it may be lethal. Identifying and interpreting breast lesions in mammograms is a complicated task, even for experienced radiologists. Mammograms may exhibit subtle and overlapping structures, making detection and accurate classification of abnormalities such as masses, calcifications, and architectural distortions difficult. The variability in lesion appearance, dense breast tissue, and the need for precise differentiation between benign and malignant conditions add to the challenge. This review compares findings and classifying two primary forms of breast anomalies: mass and microcalcification generally observed in mammograms based on the latest developments in the auto-recognition and categorization of breast cancer in mammographs. The comprehensive review presented here offers a solid introduction to the field of breast cancer classification. The basic motivation behind this review article is to study the various DL-based breast cancer detection methods proposed in recent years for mass and calcification detection and classification. It addresses the challenges associated with mass, and microcalcification detection using deep learning techniques to progress diagnostic accurateness to guide future applications.

Breast cancer early diagnosis is a major concern for reducing deadly cases. Automation of microcalcification detection is becoming increasingly important given their tiny scale. At this step, a high false negative rate is observed, leading to high ambiguity. In this context, conventional approaches are unable to handle such ambiguity. Possibility theory offers a powerful paradigm enabling to handle a high uncertainty level. Therefore, in this research,new possibilistic modeling strategy for microcalcification detection is proposed. The developed system is based on k-means clustering followed by a fusion of the corresponding possibility distributions, for decision making. For enhancing classification's accuracy, two aspects may be taken into consideration: scattering within classes and the class discrimination power. A high inter-class variance may be regarded as an evidence of good discrimination. Nevertheless, in case of existing highly scattered classes, miss-classifications of samples at the class margins are still encountered, even at high inter-class variance. Clustering solves this problem by redefining classes in a more compact way. Under this paradigm, the clustering optimization is performed using a criteria based on the Area Under Curve(AUC), where the confidence degree level is based on the consistency principle of Dubois and Prade. The above strategy has been applied for the detection of microcalcifications, based on a set of pyramidal and multi-resolution based features. Validation on the Digital Database for Screening Mammography (DDSM) public dataset has been undertaken. The proposed system, which gives 99.4 % detection accuracy, can be used to assist medical practitioners.

The main problem with all fractal compression implementation is execution time. Algorithms can spend hours to compress a single image. Most of the major variants of the standard algorithm for speeding up computation time have led to a bad-quality or a lower compression ratio. For example, the Fisher's [7] proposed classification pattern greatly accelerated the algorithm, but image quality was poor due to the search-space reduction imposed by the classification, which eleminates a lot of good solutions. By using genetic algorithms to address the problem, we optimize the domain blocks search. We explore all domain blocks present in the image but not in exhaustive way (like a standard algorithm) and without omitting any possible block (solution) as a classification pattern does. A genetic algorithm is the unique method for satisfying these constraints. And it is a way to do be a random search because the genetic one is directed by fitness selection, which produces optimal solutions. Our goal in this work is to use a genetic algorithm to solve the IFS inverse problem and to build a fractal compression algorithm based on the genetic optimization of a domain blocks search. we have also implemented standard Barnsley algorithm, the Y. Fisher based on classification, and the genetic compression algorithm with quadtree partitioning. A population of transformations was evolved for each range block, and the result is compared with the standard Barnsely algorithm and the Fisher algorithm = based classification. We deduced an optimal set of values for the best parameters combination, and we can also specify the best combination for each desired criteria: best compression ratio, best image quality, or quick compression process. By running many test images, we experimentally found the following set of optimal values of all the algorithm parameters that ensure compromise between execution time and solutions optimality: Population size = 100, Maximum generations = 20, Crossover rate = 0.7, Mutation rate = 0.1, RMS limit = 5, Decomposition error limit = 10, Flips and isometrics count = 8. In our proposed algorithm, results were much better than those obtained both vences and Rudomin [5] and Lankhorst [4] approaches.

Detection of suspicious masses in mammograms play a vital role in early diagnosis of breast cancer, to reduce the death rate among women. The presence of masses and calcification’s in mammograms is distinguishing signs for breast cancer diagnosis. But in some cases, due to contrast variation, fuzzy boundaries and presence of noise in mammograms, segmentation and detection of masses is challenging assignment. This paper presents a new segmentation approach to detect masses in mammographic images. The proposed approach consist of artifacts elimination and pectoral region extraction, suspicious mass enhancement using dual morphological operation technique and finally, extraction of Regions of Interest (ROIs) from background using scaled Reyni entropy. The proposed system has been tested on two data-sets i.e. mini- Mammographic Image Analysis Society (mini-MIAS) and Digital Database for Screening Mammography (DDSM), over 50 and 90 mammograms respectively. Performance achieved with proposed system in terms of True Positive Fraction (TPF) yields 93.2% and 93.9% respectively, at the rate of 1.48 and 0.74 average False Positive per Image (FP/I), tested on both Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) views. The obtained experimental results demonstrates that proposed method gives improved results for mass detection and can be useful for radiologists in diagnosis of breast cancer at early stage.

Breast Cancer is one of the leading causes of death, and its early detection increases the survival rate and treatment options available to patients. Computer-Aided-Detection (CAD) systems have been developed to assist radiologists with the task of locating cancer in mammograms. Unfortunately, these CAD systems have demonstrated less than 50% efficiency in detecting Architectural Distortions (AD), which is a sign of breast cancer. This paper presents a method of detecting AD with better sensitivity results. Forty mammograms containing AD were obtained from the Digital Database for Screening Mammography (DDSM). Each mammogram was preprocessed using breast segmentation techniques to extract the breast region from the mammogram. The mammograms were enhanced using contrast-limited adaptive histogram equalization (CLAHE). Next, the enhanced mammograms were filtered with a bank of 180 Gabor filters to extract the texture orientation from the images. Based on the fact that ADs contain spicules radiating in all direction, AD templates were designed in MATLAB. These templates were cross-correlated with the Gabor filtered mammograms to obtain ROIs that were most likely to contain ADs. The developed algorithm is intended to assist the radiologist by flagging regions likely to contain AD, prompting the radiologist to take a closer look at those regions. The current algorithm developed in MATLAB automatically flags seven suspicious blocks of 25 by 25 pixels per image, and demonstrated a sensitivity of 87.5% with a False Positive per Image (FPI) of 5.2. Future work will focus on the reduction of FPI.

ObjectivesTo investigate whether the increased soft tissue contrast of virtual monoenergetic images (VMIs) obtained from a spectral detector computed tomography (SDCT) system improves washout assessment of arterially hyper-enhancing liver lesions.MethodsFifty-nine arterially hyper-enhancing lesions in 31 patients (age 65 ± 9 years, M/W 20/11) were included in this IRB-approved study. All patients underwent multi-phase SDCT for HCC screening. MRI, CEUS or biopsy within 3 months served as standard of reference to classify lesions as LiRADS 3 or 4/5. VMIs and conventional images (CIs) were reconstructed. Visual analysis was performed on 40, 60, and 80 kiloelectronvolt (keV) and CIs by 3 radiologists. Presence and visibility of washout were assessed; image quality and confidence of washout evaluation were evaluated on 5-point Likert scales. Signal-to-noise ratio (SNR), lesion-to-liver contrast-to-noise ratio (CNR) (|HUlesion–HUliver|/SDliver) and washout (|HUlesion–HUliver|) were calculated. Statistical assessment was performed using ANOVA and Wilcoxon test.ResultsOn subjective lesion analysis, the highest level of diagnostic confidence and highest sensitivity for the detection of lesion washout were found for 40-keV VMIs (40 keV vs. CI, 81.3 vs. 71.3%). Image quality parameters were significantly better in low-kiloelectronvolt VMIs than in CIs (p < 0.05; e.g. SNRliver: 40 keV vs. CIs, 12.5 ± 4.1 vs. 5.6 ± 1.6). In LiRADS 4/5 lesions, CNR and quantitative washout values were significantly higher in 40-keV VMIs compared to CIs (p < 0.05; e.g. CNR and washout in 40 keV vs. CIs, 2.3 ± 1.6 vs. 0.8 ± 0.5 and 29.0 ± 19.1 vs. 12.9 ± 6.9 HU, respectively).ConclusionBy increasing lesion contrast, low-kiloelectronvolt VMIs obtained from SDCT improve washout assessment of hyper-enhancing liver lesions with respect to washout visibility and diagnostic confidence.Key Points• Low-kiloelectronvolt virtual monoenergetic images from spectral detector CT facilitate washout assessment in arterially hyper-enhancing liver lesions.• Image quality and quantitative washout parameters as well as subjective washout visibility and diagnostic confidence benefit from low-kiloelectronvolt virtual monoenergetic images.

272 Digital Breast Tomosynthesis Index (DBTI): A score to evaluate Digital Breast Tomosynthesis (DBT) device.

This article presents a comparative analysis of image compression algorithms used for rapid shooting and sending transport information. Due to the ever-increasing demands on the speed and quality of visual information transmission in logistics, special attention is paid to the most efficient compression algorithms such as JPEG, RLE, LZW, JPEG-2000, WebP, as well as the principal component method (PCA). The article covers key aspects: image quality parameters, processing speed and compression ratio, as well as their impact on the final result of data transmission. The conducted experiment comparing algorithms will clearly demonstrate which of them is the most optimal for solving problems in this field.

Mammography and finding of suspicious masses during self-examinations and clinical breast examinations form the primary screening tools for early detection of breast cancer. Mammography is essential for early detection of cancer, prior to any other means. Many abnormalities other than cancer are shown by screening mammography, leading to biopsy of the suspicious defect. Any enhancement that reduces the number of unnecessary biopsies is welcomed. Digital enhancement may aid in early detection of some patterns, such as microcalcification clusters, indicating the onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers, and early detection may enable a complete cure. The individual calcifications are hard to detect, due to their size and shape variability and the inhomogeneous background structure. Our study addresses only early detection of microcalcifications. We present an algorithm which locates microcalcifications based on local gray-scale variability, tissue structures and image statistics. The mammographs are digitally enhanced to accent textures and a previously developed threshold filter creates a binary mask of the calcification's spatial location.

A new method for detecting microcalcifications in mammograms is described. In this method, the mammogram image is first processed by a subband decomposition filterbank. The bandpass subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. The detection method utilizes these two parameters. A region with high positive skewness and kurtosis is marked as a region of interest. Simulation results show that this method is successful in detecting regions with microcalcifications.

A novel technique to detect the microcalcifications in digital mammograms presented in this paper uses the statistical measures, namely mean and variance, as the criterion to classify the pixels representing microcalcifications. This method has proved its credentials by accurately segmenting the microcalcifications in the mammogram image. This approach fixes the boundary of the microcalcifications accurately, which confirms, its qualitative performance.

Experimental investigation on combustion and emissions of a two-stroke DISI engine fueled with aviation kerosene at various compression ratios

The performance of diesel engine highly depends on atomization, vaporization and mixing of fuel with air. These factors are strongly influenced by various parameters e.g. injection pressure, injection timing, compression ratio, equivalence ratio, cylinder geometry, in cylinder air motion etc. In this study, a diesel engine has been investigated by employing a commercial CFD software (ANSYS Forte, version 18.1) especially developed for internal combustion engines (ICE) modeling; focusing primarily on the effects of equivalence ratio and compression ratio on combustion and emission characteristics. RNG k-ε model was employed as the turbulence model for analyzing the physical phenomena involved in the change of kinetic energy. In order to reduce the computational cost and time, a sector mesh of 45o angle with periodic boundary conditions applied at the periodic faces of the sector, is considered instead of using the whole engine geometry. Simulations are performed for a range of equivalence ratio varying from 0.6 to 1.2 and for three compression ratios namely, 15:1, 18:1 and 21:1. Results show that, improvement in combustion characteristics with higher compression ratio could be achieved for both lean and rich mixtures. Peak in-cylinder pressure and peak heat release nearer to TDC are achieved for compression ratio of 18:1 that could results in more engine torque. For compression ratio beyond 16:1, effects of fuel concentration on ignition delay is more pronounced. At lower compression ratio, in-cylinder temperature is not sufficiently high for atomization, vaporization, mixing of fuel with air, and preflame reactions to occur immediately after the fuel injection. NOx emission in diesel engine increases due to higher pressure and temperature inside the cylinder associated with relatively higher compression ratio. Rich mixture leads to more CO and unburnt hydrocarbon emission compared to lean mixture as result of incomplete combustion. Engine operation with too high compression ratio is detrimental as emission is a major concern.

Impact of variable compression ratios on engine performance and unregulated HC emitted from a research single cylinder engine fueled with commercial gasoline