Coordinate Logic Filters Research Articles

Microcalcifications Detection Using Image Processing

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women’s quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this work, an effective methodology to detect microcalcifications in digitized mammograms is presented. This methodology is based on the synergy of image processing, pattern recognition and artificial intelligence. The methodology consists in four stages: image selection, image enhancement and feature extraction based on mathematical morphology operations applying coordinate logic filters, image segmentation based on partitional clustering methods such as k-means and self organizing maps and finally a classifier such as an artificial metaplasticity multilayer perceptron. The proposed system constitutes a promising approach for the detection of Microcalcifications. The experimental results show that the proposed methodology can locate Microcalcifications in an efficient way. The best values obtained in the experimental results are: accuracy 99.93% and specificity 99.95%, These results are very competitive with those reported in the state of the art.

Coordinate Logic Order Statistics & Applications in Image Processing

A new approach regarding Low-Level Image & Signal processing is presented. This new approach deals with the philosophy of finding a method for designing and executing algorithms (filters), using the least possible complexity. The proposed method is based on Coordinate Logic Order Statistics filters (CL-OS) which are an extension of Coordinate Logic Filters and introduces a minimalistic approach in filter design followed by an satisfactory efficiency in terms of quality, speed, complexity, and energy consumption. CL-OS filters have similar functionality with OS filters, specifically the basis of OS filters is the sorting of the original signal values while the basis of CL-OS filters is the sorting of the binary levels of the original signal values. The intrinsic feature of CL-OS filters is their explicit low overall complexity which is $$\varvec{O}(n)$$O(n), on the other hand the general overall complexity of OS filters is $$\varvec{O}(n^{2})$$O(n2); moreover, CL-OS filters are characterized by a naturally embedded parallelism which makes them absolutely appropriate for H/W implementation and real-time applications. Another characteristic property of CL-OS is that their field of definition includes the OS's field of definition causing an increase of the signal energy and thus constituting them not only smoothers but enhancers too. The last property is of great importance since it reveals new signal information giving a different interpretation of the original signal.

Coordinate logic order statistics filters: new approach for real‐time image processing

A new approach regarding real-time low-level image and signal processing is presented. This new approach deals with the philosophy of finding a method for designing and executing algorithms (filters), using the most minimum possible complexity. The proposed method is based on a special formation of coordinate logic filters (CLFs), the coordinate logic order statistics (CL-OS) filters. CL-OS introduces a minimalistic approach in filter design followed by the greatest possible efficiency in terms of speed, quality, complexity and energy consumption. Moreover CL-OS reveals new signal information; this information gives a different interpretation of the original signal, it could be said that it acts like a function applying a nonlinear quantisation in order to approximate the desired output.

Implementation of morphological filters using coordinate logic operations

In this paper we present a methodology for performing morphological operations using coordinate logic operations. Coordinate logic operations are simple and fast because they are logic operations among the corresponding binary values of the image pixels. Coordinate logic (CL) filters are a family of fast non-increasing, non-linear filters, which are based on coordinate logic operations. CL filters exhibit analogous properties to those of the morphological filters but due to their different definition they display slightly different response. This is revoked in this paper by using appropriate image gray level quantization and mapping of the resulting levels to a specific set of decimal numbers. Based on this approach some fast efficient implementations of morphological filters using CL operators are proposed.

Coordinate logic filters and their applications in image processing and pattern recognition

In this paper we present a number of image processing applications using coordinate logic filters, which execute coordinate logic operations among the pixels of the image. These filters are very efficient in various 1D, 2D, or higher-dimensional digital signal processing applications, such as noise removal, magnification, opening, closing, skeletonization, and coding, as well as in edge detection, feature extraction, and fractal modeling. In this paper we present some typical image processing applications using coordinate logic filters. The key issue in the coordinate logic analysis of images is the method of fast successive filtering and managing of the residues. The desired processing is achieved by executing only direct logic operations among the pixels of the given image. Coordinate logic filters can be easily and quickly implemented using logic circuits or cellular automata; this is their primary advantage.