Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Analisis Tekstur untuk Klasifikasi Motif Kain (Studi Kasus Kain Tenun Nusa Tenggara Timur)

AbstrakIndonesia memiliki banyak kekayaan budaya dalam bentuk kain tradisional, salah satunya kain tenun dari Nusa Tenggara Timur (NTT). Kain tenun dari tiap etnik di NTT memiliki cirikhas motif masing-masing yang merupakan manifestasi kehidupan sehari-hari, kebudayaan dan kepercayaan masyarakat setempat. Di mata pemerhati kain tenun NTT, asal kain tenun dapat diketahui dari motifnya. Tidak semua orang dapat membedakan asal daerah dari motif kain tenun tertentu dikarenakan sulitnya mendefinisikan karakteristik motif kain tenun suatu daerah dan beragamnya motif kain tenun yang ada dan komposisi warna yang beragam pula.Analisis tekstur adalah teknik analisis citra berdasarkan anggapan bahwa citra dibentuk oleh variasi intensitas piksel, baik citra keabuan maupun warna. Motif kain tenun terbentuk dari variasi intensitas warna sehingga dapat dipandang sebagai tekstur berwarna dari kain tenun. Penelitian ini bertujuan untuk mengetahui diantara pendekatan analisis tekstur menggunakan Gray Level Co-occurrence Matrix (GLCM) yang dikombinasikan dengan momen warna dan pendekatan analisis tekstur menggunakan Color Co-occurrence Matrix (CCM), metode manakah yang memberikan hasil lebih baik untuk klasifikasi motif kain tenun NTT.Hasil penelitian menunjukkan bahwa untuk klasifikasi motif kain tenun NTT, pendekatan analisis tekstur menggunakan metode CCM memberikan hasil lebih baik dibandingkan pendekatan analisis tekstur menggunakan GLCM yang dikombinasikan dengan momen warna. Kata kunci—klasifikasi citra, GLCM, CCM, momen warna, motif kain tenun NTT AbstractIndonesia have many culture in the form of traditional fabrics, one of them is woven fabric from Nusa Tenggara Timur (NTT). Each NTT ethnic has motif characteristic which ismanifestation of daily life, culture and the faith of local people. For a NTT woven fabric observer, the origin of a woven fabric can be known from the motif. But its difficult to recognising the origin of a woven fabrics because it is hard to define the characteristics of woven fabric motif from a region and wide variety of existing woven fabric motifs and also color composition.Texture analysis is image analysis technique based on assumption that an image formed by the variation of pixels intensity, both gray and color image. Woven fabric motif formed by the variation of color intensity that can be seen as color texture of the woven fabric. This study aims to determine between texture analysis using GLCM combined with color moment and texture analysis using CCM, which method gives better results for the NTT woven fabric motif classification.The results showed that for the NTT woven fabric motif classification, texture analysis using CCM gives better results than the texture analysis using GLCM combined with color moment. Keywords— image classification, GLCM, CCM, color moment, NTT woven fabric moti

Unsupervised skin lesion classification and matching

According to the American Cancer Society (ACS), since 1973, the mortality rate for melanoma has increased by 44%. The number of serious skin cancers diagnosed has also more than doubled in that same period. Even though serious skin cancers (melanoma) account for only 4% of skin cancer diagnoses (and skin cancer is the most common cancer) it is responsible for almost all (79%) cancer deaths. The ACS reports about 7,300 people in the United States are expected to die of melanomas in 2002, other sources put the number as high as 7,800. There are about 130,000 cases of melanoma worldwide, and about 37,000 related deaths. Many physicians think the increase in melanoma diagnoses represents an epidemic. Currently, there is work to improve diagnostics once a lesion comes under suspicion, and there are also systems to do whole body images of skin lesions. Where there seems to be a gap is in tracking and classifying the lesions in image histories. The critical problem is not so much how to treat the lesion once its discovered, but to detect it in the first place. In addition, in the classification systems encountered, there didn\u27t seem to be any using all combinations of color, texture, and shape, any or all of which can help detect a malignant growth. Since almost all lesions are slow-growing, and very often on the back, it can be difficult for both patient and doctor to detect when a lesion has begun to change, which is one of the first warning signs of skin cancer. This work is comprised of an analysis system written in Matlab, which pre-processes the image, removing background artifacts via morphological operations to segment the lesion. The lesion is then processed for shape, color content, and texture. This occurred for a small database of images comprising melanomas, dysplastic nevi, and moles, and 10 feature vectors were captured for each image along with the filename and matching diagnosis. Additional images were procured from the web, and also from photographs of individuals using a Cannon EOS Rebel G, which were scanned in using an Acer ScanPrisa 640U. These images were then processed with the same software used for the database images. The results were classified based on these feature vectors and assigned a FWL (Feature Warning Level). Lastly, the input results were compared to the database for matches within a range for similarity. The closest match (if within a reasonable range) is reported. This system could be attached to existing tracking systems (like MoleMap) or used as a stand alone tracking tool for dermatologists. Any change in one of the feature vectors, or in a group of features could trigger a closer look by the physician. According to literature, and a dialog with a dermatologist, history is the one of the most critical factors in early detection, when the cancer can be completely cured

VSG image processing and analysis (VSG IPA) toolbox

The VSG TOOLBOX is a free MATLAB compatible image processing and analysis toolbox which provides high level access to a wide range of image processing and analysis algorithms. This toolbox requires MathWorks' MATLAB 7.1 or later BUT DOES NOT REQUIRE the MathWorks' Image Processing Toolbox. This report outlines the mechanism by which you can interface MATLAB with the VSG IPA toolbox functions. This free software can be downloaded directly from: http://www.cipa.dcu.ie/code.htm

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin