Comparison of Gradient-Based Edge Detectors Applied on Mammograms

Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: pre-processing. That is, identifying the image orientation, detecting the breast and eliminating irrelevant parts. This paper aims to describe, analyze, compare and evaluate six of the most commonly used edge detection operators: Sobel, Roberts Cross, Prewitt, Farid and Simoncelli, Scharr and Canny. We detail the algorithms, their implementations and the metrics used for evaluation and continue by comparing the operators both visually and numerically, finally concluding that Canny best suit our needs.

Investigation of field output factors using IAEA-AAPM TRS-483 code of practice recommendations and Monte Carlo simulation for 6 MV photon beams

Introduction: This study aims to experimentally determine field output factors using the methodologies suggested by the IAEA-AAPM TRS-483 for small field dosimetry and compare with the calculation from Monte Carlo (MC) simulation. Methods: The IBA-CC01, Sun Nuclear EDGE and IBA-SFD detectors were employed to determine the uncorrected and the corrected field output factors for 6 MV photon beams. Measurements were performed at 100 cm source to axis distance, 10 cm depth in water, and the field sizes ranged from 1 × 1 to 10 × 10 cm2. The use of field output correction factors proposed by the TRS-483 was utilised to determine field output factors. The measured field output factors were compared to that calculated using the egs_chamber user code. Results: The decrease in the percentage standard deviation of the measured three detectors was observed after applying the field output correction factors. Measured field output factors using CC01 and EDGE detectors agreed with MC values within 3% for field sizes down to 1 × 1 cm2, except the SFD detector. Conclusions: The corrected field output factors agree with the calculation from MC, except the SFD detector. CC01 and EDGE are suitable for determining field output factors, while the SFD may need more implementation of the intermediate field method.

Feature Extraction in Edge Detection using Genetic Programming

<p>Edge detection is important in image processing. Extracting edge features is the main and necessary process in edge detection. Since features in edge detection are implicit, most of the existing edge features only work well on specific images. Using a moving window has a trade-off between noise rejection and localisation accuracy. Genetic Programming (GP) has been widely applied to image processing, and GP has potential for extracting edge features, although there is little work in GP for edge detection. The overall goal of this thesis is to investigate GP for automatic edge feature extraction using different amounts of existing knowledge from only using raw pixel intensities and ground truth to more advanced domain knowledge such as Gaussian filters. First of all, this thesis conducts an investigation on fundamental low-level edge detector construction with very little prior edge knowledge. Search operators based on a single raw pixel, a block of pixels, and two blocks of pixels are proposed to construct edge detectors. Unlike most existing methods, this GP system automatically searches neighbours and avoids manually predefining a window size. The results show that the evolved edge detectors outperform some existing edge detectors, such as the Sobel edge detector. Secondly, from the pixel and image views, localisation of detected edges, and observations of GP programs, new fitness functions are suggested in this thesis. It is found that the pixel view is better than the image view to design fitness functions without allowing a distance from predictions to ground truth. However, in terms of edge localisation, the pixel view is worse than the image view to design fitness functions. A new fitness function combining detection accuracy and localisation effectively improves the performance of evolved edge detectors. When utilising observations of GP programs to construct soft edge maps, two new fitness functions including a restriction on the range of observations are proposed to evolve edge detectors with good soft edge maps on test images. Thirdly, pixels implicitly selected by the GP system based on full images are analysed. A set of pixels are extracted from the evolved programs and used to construct edge filters. A merge operation is proposed to extract six pixels to construct second-order edge filters. The results show that a rich but compact set of pixels can be extracted from the evolved edge detectors. Fourthly, GP is utilised to evolve edge detectors based on the Gaussian-based technique. These GP evolved edge detectors are significantly better than the Gaussian gradient and the surround suppression technique. An efficient and effective sampling technique is proposed for evolving Gaussian-based edge detectors. From the results, there are no significant differences between the Gaussian-based edge detectors evolved by a full set of images and by the sampling technique on the training set. Fifthly, GP is employed to construct features using an existing set of basic features. The distribution of observations of GP programs is estimated. Evolved composite features are proposed using known distribution models to indicate the probability of pixels being discriminated as edge points. It is found that the composite features effectively combine advantages of basic features and can richly indicate edge responses. Finally, a Bayesian-based GP system is proposed to construct high-level edge features via employing two general algebraic operators and a function developed from a simple Bayesian model. The simple Bayesian model utilises a general multivariate normal density to combine basic features. Experiments show that the GP evolved programs perform better than the simple Bayesian model to obtain composite features. Overall, this thesis shows that GP has the capability to effectively extract edge features using different degrees of prior knowledge about edges.</p>

Feature Extraction in Edge Detection using Genetic Programming

<p>Edge detection is important in image processing. Extracting edge features is the main and necessary process in edge detection. Since features in edge detection are implicit, most of the existing edge features only work well on specific images. Using a moving window has a trade-off between noise rejection and localisation accuracy. Genetic Programming (GP) has been widely applied to image processing, and GP has potential for extracting edge features, although there is little work in GP for edge detection. The overall goal of this thesis is to investigate GP for automatic edge feature extraction using different amounts of existing knowledge from only using raw pixel intensities and ground truth to more advanced domain knowledge such as Gaussian filters. First of all, this thesis conducts an investigation on fundamental low-level edge detector construction with very little prior edge knowledge. Search operators based on a single raw pixel, a block of pixels, and two blocks of pixels are proposed to construct edge detectors. Unlike most existing methods, this GP system automatically searches neighbours and avoids manually predefining a window size. The results show that the evolved edge detectors outperform some existing edge detectors, such as the Sobel edge detector. Secondly, from the pixel and image views, localisation of detected edges, and observations of GP programs, new fitness functions are suggested in this thesis. It is found that the pixel view is better than the image view to design fitness functions without allowing a distance from predictions to ground truth. However, in terms of edge localisation, the pixel view is worse than the image view to design fitness functions. A new fitness function combining detection accuracy and localisation effectively improves the performance of evolved edge detectors. When utilising observations of GP programs to construct soft edge maps, two new fitness functions including a restriction on the range of observations are proposed to evolve edge detectors with good soft edge maps on test images. Thirdly, pixels implicitly selected by the GP system based on full images are analysed. A set of pixels are extracted from the evolved programs and used to construct edge filters. A merge operation is proposed to extract six pixels to construct second-order edge filters. The results show that a rich but compact set of pixels can be extracted from the evolved edge detectors. Fourthly, GP is utilised to evolve edge detectors based on the Gaussian-based technique. These GP evolved edge detectors are significantly better than the Gaussian gradient and the surround suppression technique. An efficient and effective sampling technique is proposed for evolving Gaussian-based edge detectors. From the results, there are no significant differences between the Gaussian-based edge detectors evolved by a full set of images and by the sampling technique on the training set. Fifthly, GP is employed to construct features using an existing set of basic features. The distribution of observations of GP programs is estimated. Evolved composite features are proposed using known distribution models to indicate the probability of pixels being discriminated as edge points. It is found that the composite features effectively combine advantages of basic features and can richly indicate edge responses. Finally, a Bayesian-based GP system is proposed to construct high-level edge features via employing two general algebraic operators and a function developed from a simple Bayesian model. The simple Bayesian model utilises a general multivariate normal density to combine basic features. Experiments show that the GP evolved programs perform better than the simple Bayesian model to obtain composite features. Overall, this thesis shows that GP has the capability to effectively extract edge features using different degrees of prior knowledge about edges.</p>

BUILDING ROOF VECTORIZATION WITH PPGNET

A challenge in data-based 3D building reconstruction is to find the exact edges of roof facet polygons. Although these edges are visible in orthoimages, convolution-based edge detectors also find many other edges due to shadows and textures. In this feasibility study, we apply machine learning to solve this problem. Recently, neural networks have been introduced that not only detect edges in images, but also assemble the edges into a graph. When applied to roof reconstruction, the vertices of the dual graph represent the roof facets. In this study, we apply the Point-Pair Graph Network (PPGNet) to orthoimages of buildings and evaluate the quality of the detected edge graphs. The initial results are promising, and adjusting the training parameters further improved the results. However, in some cases, additional work, such as post-processing, is required to reliably find all vertices.

Data-driven edge detectors for seismic data interpretation

We present a novel method to assist in seismic interpretation. The algorithm learns data-driven edge-detectors for structure enhancement when applied to time slices of 3D poststack seismic data. We obtain the operators by distilling the local and structural information retrieved from patches taken randomly from the input time slices. The filters conform to an orthogonal family that behaves as structure-aware Sobel-like edge detectors, and the user can set their size and number. The results from marine Canada and New Zealand 3D seismic data demonstrate that the proposed algorithm allows the semblance attribute to improve the delineation of the subsurface channels. This fact is further supported by testing the method with realistic synthetic 2D and 3D data sets containing channeling and meandering systems. We contrast the results with standard plain Sobel filtering, multidirectional Sobel filters of variable size, and the dip-oriented plane-wave destruction Sobel attribute. The proposed method gives results that are comparable or superior to those of Sobel-based approaches. In addition, the obtained filters can adapt to the geological structures present in each time slice, which reduces the number of unwanted artifacts in the final product.

Identification of gold mineralization zones of low sulfidation epithermal systems using geoelectrical and magnetic methods in Ciparay area, Cibaliung

Abstrak. Cibaliung merupakan daerah pertambangan mineral yang berada di Provinsi Banten. Hal ini, dibuktikan dengan adanya lubang tambang emas di daerah Cikoneng dan Cibitung. Penelitian tentang geofisika penting dilakukan guna menemukan cadangan emas baru di daerah Ciparay yang terletak di Sebelah Tenggara Cikoneng dan Cibitung. Metode geofisika yang digunakan di antaranya magnetik, resistivitas, dan induced polarization (IP). Metode magnetik digunakan sebagai survei pendahuluan untuk menggambarkan keberadaan struktur geologi pengontrol mineralisasi emas. Melalui peta reduce to pole dapat diketahui adanya tanda-tanda keberadaan struktur geologi yang ditunjukkan oleh anomali negatif (-220 hingga -135 nT) di Bagian Barat Daya daerah penelitian. Hasil teknik edge detectors menunjukkan adanya pola struktur dengan arah Northwest (NW) dan North-Northeast (NNE) yang dominan berada di Bagian Barat Daya sebelah Utara daerah penelitian. Metode resistivitas dan IP digunakan sebagai survei detail untuk menentukan keberadaan mineral yang terkandung dalam batuan. Hasilnya menunjukkan bahwa zona potensi mineralisasi ditunjukkan oleh anomali tinggi (resistivitas 50 ohm.m dan chargeability 40 msec). Resistivitas tinggi diduga sebagai respons batuan induk andesitic sedangkan, nilai chargeability tinggi merupakan respons dari hadirnya mineral-mineral bijih seperti emas dan perak. Zona potensi mineralisasi berada pada posisi patok 350-800 dengan arah persebaran mengikuti arah struktur geologi pengontrolnya yaitu NW dan NNE. Abstract. Cibaliung is a mineral mining area located in Banten Province. The area including gold mining in Cikoneng and Cibitung areas. Geophysical research is important to find new gold reserves at the Ciparay area, located in the Southeast of Cikoneng and Cibitung. Geophysical methods used include magnetic, resistivity, and IP. The magnetic method was applied as a preliminary survey to delineate the presence of the geological structure controlling the gold mineralization. Based on the RTP map, signs of the presence of geological structures are shown by anomalies -220 to -135 nT in the Southwestern part of the study area. The results of edge detector techniques show the existence of structural patterns in the direction of NW and NNE which are dominant in the Southwestern North of the study area. The resistivity and IP methods are employed for detailed investigation in order to obtain to determine the presence of minerals contained in rocks. The results show that the mineralized zones are indicated by high resistivity ( 50 ohm.m) and high chargeability ( 40 msec). High resistivity response is caused by andesitic source rock whereas, high chargeability response is related to the presence of ore minerals such as gold and silver. The mineralization prospect zone is indicated at the position of 350-800 and its direction corresponds to the direction of its geological structure namely NW and NNE.Keywords: New gold reserves, Negative magnetic anomalies, High resistivity, High chargeability.

Comprehensive Analysis of Edge Detection in Color Image Processing

Various approaches to edge detection for color images, including techniques extended from monochrome edge detection as well as vector space approaches, are examined. In particular, edge detection techniques based on vector order statistic operators and difference vector operators are studied in detail. Numerous edge detectors are obtained as special cases of these two classes of operators. The effect of distance measures on the performance of different color edge detectors is studied by employing distance measures other than the Euclidean norm. Variations are introduced to both the vector order statistic opera-tors and the difference vector operators to improve noise performance. They both demonstrate the ability to attenuate noise with added algorithm complexity. Among them, the difference vector operator with adaptive filtering shows the most promising results. Other vector directional filtering techniques are also introduced and utilized for color edge detection. Both quantitative and subjective tests are performed in evaluating the performance of the edge detectors, and a detailed comparison is presented.<div>Copyright 1999 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.<br></div><div><br></div>