ObjectivesThe aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set.Materials and MethodsIn this retrospective,

Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer.

Repeated elements are ubiquitous and abundant in both manmade and natural scenes. Editing such images while preserving the repetitions and their relations is nontrivial due to overlap, missing parts, deformation across instances, illumination variation, etc. Manually enforcing such relations is laborious and error-prone. We propose a novel framework where user scribbles are used to guide detection and extraction of such repeated elements. Our detection process, which is based on a novel boundary band method, robustly extracts the repetitions along with their deformations. The algorithm only considers the shape of the elements, and ignores similarity based on color, texture, etc. We then use topological sorting to establish a partial depth ordering of overlapping repeated instances. Missing parts on occluded instances are completed using information from other instances. The extracted repeated instances can then be seamlessly edited and manipulated for a variety of high level tasks that are otherwise difficult to perform. We demonstrate the versatility of our framework on a large set of inputs of varying complexity, showing applications to image rearrangement, edit transfer, deformation propagation, and instance replacement.

RepFinder : Finding Approximately Repeated Scene Elements for Image Editing

Pulse-coupled neural networks (PCNN) have an inherent ability to process the signals associated with the digital visual images because it is inspired from the neuronal activity in the primary visual area, V1, of the neocortex. This paper provides insight into the internal operations and behaviors of PCNN, and reveals the way how PCNN achieves good performance in digital image processing. The various properties of PCNN are categorized into a novel three-dimensional taxonomy for image processing mechanisms. The first dimension specifies the time matrix of PCNN, the second dimension captures the firing rate of PCNN, and the third dimension is the synchronization of PCNN. Many examples of processing mechanisms are provided to make it clear and concise.

Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review

Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach

Breast cancer continues to be one of the major health issues across the world and it is mostly observed in females. However, the actual cause of this cancer is still an ongoing research topic. Hence, early detection and diagnosis of breast cancer are considered to be an effective and reliable solution. Mammography is one of the most efficacious medical tools for early detection of breast cancer. The radiologists identify the suspicious regions in the breast by carefully examining the mammograms. However, mammograms are sometimes difficult to analyze when the breast tissues are dense. Therefore, a computer-aided diagnosis (CAD) system is adopted which can improve the decisions of the radiologists. This paper proposes a hybrid CAD framework to classify the suspicious regions into either normal or abnormal, and further, benign or malignant. The proposed framework constitutes four computational modules, namely, ROI generation using cropping operation, texture feature extraction using contourlet transformation, a wrapper-based feature selection algorithm, namely, forest optimization algorithm to select the optimal features, and finally different classifiers like SVM, k-NN, Naive Bayes, and C4.5 that are employed to classify the inputs into normal or abnormal, and again benign or malignant. The proposed framework is examined on two widely used standard datasets, namely, MIAS and DDSM. The performance measures are computed with respect to normal vs. abnormal, and benign vs. malignant for four different hybrid CAD models, namely, (Contourlet + FOA + SVM), (Contourlet + FOA + k-NN), (Contourlet + FOA + Naive Bayes), and (Contourlet + FOA + C4.5). The highest classification accuracy of 100% is achieved for normal vs. abnormal classification in case of both MIAS and DDSM. The performance of the proposed hybrid scheme demonstrates its effectiveness with the other state-of-the-art schemes. Experimental results reveal that the proposed hybrid scheme is accurate and robust. Finally, the suggested scheme is considered as a reliable CAD framework to help the physicians for better diagnosis.

Mammogram classification using contourlet features with forest optimization-based feature selection approach

A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN

Detection of visually salient image regions is extensively applied in computer vision and computer graphics, such as object detection, adaptive compression, and object recognition, but any single model always has its limitations to various images, so in our work, we establish a method based on multimodel saliency maps to detect the object, which intelligently absorbs the merits of various individual saliency detection models to achieve promising results. The method can be roughly divided into three steps: in the first step, we propose a decision-making system to evaluate saliency maps obtained by seven competitive methods and merely select the three most valuable saliency maps; in the second step, we introduce heterogeneous PCNN algorithm to obtain three prime foregrounds; and then a self-designed nonlinear fusion method is proposed to merge these saliency maps; at last, the adaptive improved and simplified PCNN model is used to detect the object. Our proposed method can constitute an object detection system for different occasions, which requires no training, is simple, and highly efficient. The proposed saliency fusion technique shows better performance over a broad range of images and enriches the applicability range by fusing different individual saliency models, this proposed system is worthy enough to be called a strong model. Moreover, the proposed adaptive improved SPCNN model is stemmed from the Eckhorn’s neuron model, which is skilled in image segmentation because of its biological background, and in which all the parameters are adaptive to image information. We extensively appraise our algorithm on classical salient object detection database, and the experimental results demonstrate that the aggregation of saliency maps outperforms the best saliency model in all cases, yielding highest precision of 89.90%, better recall rates of 98.20%, greatest F -measure of 91.20%, and lowest mean absolute error value of 0.057, the value of proposed saliency evaluation E H A reaches to 215.287. We deem our method can be wielded to diverse applications in the future.

Object detection system based on multimodel saliency maps

In this paper, a new multi-objective cat swarm optimization algorithm has been proposed. The algorithm applies the part of individuals into the seeking mode and the other part of individuals into the tracing mode non-randomly. Cat map is used to initialize individuals of population. In this way, individuals can avoid trapping into local optimal in the final iteration process and the search ability of algorithm can be improved effectively. The performance of proposed method is testified by using 4 multi-objective test functions. A quantitative assessment of proposed method is carried out using several performances metrics and compared with the previous design methods NSGA-II and MOPSO. The experiments illustrate that the proposed algorithm is better than the other algorithms.

Chongfan Luo

Papers

A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN

Object detection system based on multimodel saliency maps

A non-random multi-objective cat swarm optimization algorithm based on CAT MAP