Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201

Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images

International audience—In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by non-parametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines (SVMs) and Random Forest (RF) are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended attribute profiles (EAPs)

Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes

International audience—Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that at the present can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators (e.g., attribute filters) have proven their effectiveness in processing very high resolution images. Attribute filters are based on attributes which can be efficiently implemented on tree-based image representations. In this work, we considered the definition of min, max, direct and subtractive filter rules for the computation of attribute filters over the tree of shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas

Discrete Morphological Neural Networks

A classical approach to designing binary image operators is Mathematical Morphology (MM). We propose the Discrete Morphological Neural Networks (DMNN) for binary image analysis to represent W-operators and estimate them via machine learning. A DMNN architecture, which is represented by a Morphological Computational Graph, is designed as in the classical heuristic design of morphological operators, in which the designer should combine a set of MM operators and Boolean operations based on prior information and theoretical knowledge. Then, once the architecture is fixed, instead of adjusting its parameters (i.e., structural elements or maximal intervals) by hand, we propose a lattice gradient descent algorithm (LGDA) to train these parameters based on a sample of input and output images under the usual machine learning approach. We also propose a stochastic version of the LGDA that is more efficient, is scalable and can obtain small error in practical problems. The class represented by a DMNN can be quite general or specialized according to expected properties of the target operator, i.e., prior information, and the semantic expressed by algebraic properties of classes of operators is a differential relative to other methods. The main contribution of this paper is the merger of the two main paradigms for designing morphological operators: classical heuristic design and automatic design via machine learning. Thus, conciliating classical heuristic morphological operator design with machine learning. We apply the DMNN to recognize the boundary of digits with noise, and we discuss many topics for future research

Retinal Fundus Anjiyografi Görüntülerinde Drusen Alanlarının Otomatik Tespiti ve Hesaplanması

Computer aided detection (CAD) systems are widely used in the analysis of biomedical images. In this paper, we present a novel CAD system to detect age-related macular degeneration (ARMD) on retinal fundus fluorescein angiography (FFA) images, and we provide an areal size calculation of pathogenic drusen regions. The purpose of this study is to enable identification and areal size calculation of ARMD-affected regions with the developed CAD system; hence, we aim to discover the condition of the disease as well as facilitate long-term patient follow-up treatment. With the aid of this system, assessing the marked regions will take less time for ophthalmologists and observing the progress of the treatment will be a simpler process. The CAD system consists of four stages, a) preprocessing, b) segmentation, c) region of interest detection and d)feature extraction and drusen area detection. Detection through CAD and calculation of drusen regions were performed with a dataset composed of 75 images. The results obtained from the developed CAD system were examined by a specialist ophthalmologist, and the performance criteria of the CAD system are reported as conclusions. As a result, with 66 correct detections and 9 incorrect detections, the developed CAD system achieved an accuracy rate of 88%.Bilgisayar destekli tespit (BDT) sistemleri biyomedikal görüntülerin analizinde geniş bir kullanım alanına sahiptir. Bu çalışmada retinal fundus anjiyografi görüntüleri üzerinde yaşa bağlı makula dejenerasyonu (YBMD) hastalığının tespiti için bir BDT sistemi gerçekleştirilmiş ve patojenik drusen alanlarının büyüklüğünün hesaplanması sağlanmıştır. Çalışmanın amacı YBMD hastalığının görüldüğü alanların tespitinin ve büyüklüğünü hesaplamanın yanında hastalığa karşı uygulanan tedavinin sonucunun takibini de sağlamaktır. Geliştirilen sistemin yardımıyla optalmoloji uzmanları işaretlenen alanları kısa sürede tespit edebilecek ve hastalığın tedaviye verdiği cevabı basit bir şekilde gözlemleyebileceklerdir. Geliştirilen BDT sistemi 4 aşamadan oluşmaktadır, a) önişleme aşaması, b) bölütleme aşaması, c) ilgi alanı tespiti ve d) öznitelik çıkarma ve tespit aşaması. Geliştirilen BDT sistemi 75 görüntüden oluşan bir verisetiyle test edilmiştir. BST sisteminin elde ettiği sonuçlar bir optalmoloji uzmanıyla karşılaştırılarak sonuç bölümünde sunulmuştur. Geliştirilen BDT sistemi 66 doğru, 9 hatalı tespit yaparak %88 doğruluk oranı sağlamıştır

Intercomparison of medical image segmentation algorithms

Magnetic Resonance Imaging (MRI) is one of the most widely-used high quality imaging techniques, especially for brain imaging, compared to other techniques such as computed tomography and x-rays, mainly because it possesses better soft tissue contrast resolution. There are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including noise, partial volume effects and intensity inhomogeneities. Brain, being a very complicated structure, its precise segmentation is particularly necessary to delineate the borders of anatomically distinct regions and possible tumors. Many algorithms have been proposed for image segmentation, the most important being thresholding, region growing, and clustering methods such as k-means and fuzzy c-means algorithms. The main objective of this project was to investigate a representative number of different algorithms and compare their performance. Image segmentation algorithms, including thresholding, region growing, morphological operations and fuzzy c-means were applied to a selection of simulated and real brain MRI images, and the results compared. The project was realized by developing algorithms using the popular Matlab® software package. Qualitative comparisons were performed on real and simulated brain images, while quantitative comparisons were performed on simulated brain images, using a variety of different parameters, and results tabulated. It was found that the fuzzy c-means algorithm performed better than all the other algorithms, both qualitatively and quantitatively. After comparing the performance of all algorithms, it was concluded that, by combining one or two basic algorithms, a more effective algorithm could be developed for image segmentation that is more robust to noise, considers both intensity and spatial characteristics of an image, and which is computationally efficient.Magnetic Resonance Imaging (MRI) is one of the most widely-used high quality imaging techniques, especially for brain imaging, compared to other techniques such as computed tomography and x-rays, mainly because it possesses better soft tissue contrast resolution. There are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including noise, partial volume effects and intensity inhomogeneities. Brain, being a very complicated structure, its precise segmentation is particularly necessary to delineate the borders of anatomically distinct regions and possible tumors. Many algorithms have been proposed for image segmentation, the most important being thresholding, region growing, and clustering methods such as k-means and fuzzy c-means algorithms. The main objective of this project was to investigate a representative number of different algorithms and compare their performance. Image segmentation algorithms, including thresholding, region growing, morphological operations and fuzzy c-means were applied to a selection of simulated and real brain MRI images, and the results compared. The project was realized by developing algorithms using the popular Matlab® software package. Qualitative comparisons were performed on real and simulated brain images, while quantitative comparisons were performed on simulated brain images, using a variety of different parameters, and results tabulated. It was found that the fuzzy c-means algorithm performed better than all the other algorithms, both qualitatively and quantitatively. After comparing the performance of all algorithms, it was concluded that, by combining one or two basic algorithms, a more effective algorithm could be developed for image segmentation that is more robust to noise, considers both intensity and spatial characteristics of an image, and which is computationally efficient