{"id":93,"date":"2017-03-16T17:33:59","date_gmt":"2017-03-16T17:33:59","guid":{"rendered":"https:\/\/fei-lab.org\/?p=93"},"modified":"2023-12-28T20:01:32","modified_gmt":"2023-12-28T20:01:32","slug":"image-classification","status":"publish","type":"post","link":"https:\/\/fei-lab.org\/image-classification\/","title":{"rendered":"Image Classification"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-ad61b05c3455e67a2e8121f694beca4c\">\n#top .av-special-heading.av-av_heading-ad61b05c3455e67a2e8121f694beca4c{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-ad61b05c3455e67a2e8121f694beca4c .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-ad61b05c3455e67a2e8121f694beca4c .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-ad61b05c3455e67a2e8121f694beca4c av-special-heading-h3  avia-builder-el-0  el_before_av_textblock  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Image Classification<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-xx7p-b327495df8403ee8bb3facafe4b1467e '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p align=\"justify\">Image classification is a process which partitions an image into a set of distinct classes with uniform or homogeneous attributes such as textures or intensity. Classification of images can be challenging because they are affected by multiple factors such as noise, poor contrast, intensity inhomogeneity, and partial volume effects. Classification methods can be categorized as supervised and unsupervised methods. Supervised classification methods depend on the examples of information classes in the images and derive a prior model or parameters from the training sets. Unsupervised methods examine the images without specific training data and divide the image pixels into groups presented in the pixel values according to the classification criteria.<\/p>\n<p align=\"justify\"><b class=\"heading\">Multiscale Image Classification<\/b><\/p>\n<p align=\"justify\">We developed a multiscale fuzzy c-means classification method for MR images. We used an anisotropic filter to effectively attenuate the noise within the images while still preserving the edges between different tissue types. A scale space was generated using anisotropic filtering, and the general structure information was kept in the images at a coarser scale. The classification was advanced along the scale space to include local information in the fine-level images. The result from a coarser scale provides the initial parameter for the classification in the next scale. Meanwhile, the pixels with a high probability of belonging to one class in the coarse scale will belong to the same class in the next level. Therefore, these pixels in the coarser images are considered as pixels with a known class and are used as the training data to constrain the classification in the next scale. In this way, we obtain accurate classifications step-by-step and avoid being trapped into local minima. Furthermore, we also include a regulation term which constrains the pixel so that it can be influenced by its immediate neighborhoods.<\/p>\n<div id=\"attachment_94\" style=\"width: 523px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-94\" class=\"size-full wp-image-94\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/scale_Class_brain_result.jpg\" alt=\"\" width=\"513\" height=\"318\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/scale_Class_brain_result.jpg 513w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/scale_Class_brain_result-300x186.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/scale_Class_brain_result-450x279.jpg 450w\" sizes=\"auto, (max-width: 513px) 100vw, 513px\" \/><p id=\"caption-attachment-94\" class=\"wp-caption-text\">Figure 8. Classification of brain MR image volumes. Results of two slices are showed in two rows: (a) the original MR images; (b) MR images after correction of MR intensity inhomogeneity; (c) is the classification where three gray levels represent CSF, gray matter and white matter; and (d) manual segmentations for validation of the classification method.<\/p><\/div>\n<div id=\"attachment_95\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-95\" class=\"size-full wp-image-95\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/mscale_class_result_Fig.jpg\" alt=\"\" width=\"500\" height=\"484\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/mscale_class_result_Fig.jpg 500w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/mscale_class_result_Fig-300x290.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/mscale_class_result_Fig-36x36.jpg 36w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/mscale_class_result_Fig-450x436.jpg 450w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><p id=\"caption-attachment-95\" class=\"wp-caption-text\">Figure 9. The multi-scale classification software.<\/p><\/div>\n<p><b class=\"heading\">Gaussian Mixture Model-based Classification<\/b><\/p>\n<p>We implemented an unsupervised classification method based on a finite Gaussian mixture model. The method assumes a Gaussian distribution for the intensity of each class. A single pixel is assigned to these classes with a probability based on image intensity. The problem is to determine the classification that optimally fits the image histogram, and it is solved using the expectation-maximization (EM) method. The final outcome is the probability of a pixel belonging to a class and the Gaussian description of each class intensity probability distribution. In order to take into account the strong spatial correlation between pixels, the Markov random field (MRF) is introduced into the classification. The Markov random field constrains the classification of a pixel depending on the major class of its neighboring pixels. This prior probability information is combined into the Bayesian framework as the Gibbs distribution for the EM algorithm.<\/p>\n<div id=\"attachment_96\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-96\" class=\"size-full wp-image-96\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Gaussian_Class_Brain_Result.jpg\" alt=\"\" width=\"500\" height=\"184\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Gaussian_Class_Brain_Result.jpg 500w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Gaussian_Class_Brain_Result-300x110.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Gaussian_Class_Brain_Result-450x166.jpg 450w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><p id=\"caption-attachment-96\" class=\"wp-caption-text\">Figure 10. Classification results of brain MR images. (a) The original MR image, (b) the classified result, and (c) the ground truth for comparison.<\/p><\/div>\n<div id=\"attachment_97\" style=\"width: 460px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-97\" class=\"size-full wp-image-97\" src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Mixture_Class_Result_Fig.jpg\" alt=\"\" width=\"450\" height=\"446\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Mixture_Class_Result_Fig.jpg 450w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Mixture_Class_Result_Fig-80x80.jpg 80w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Mixture_Class_Result_Fig-300x297.jpg 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Mixture_Class_Result_Fig-36x36.jpg 36w, https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/Mixture_Class_Result_Fig-120x120.jpg 120w\" sizes=\"auto, (max-width: 450px) 100vw, 450px\" \/><p id=\"caption-attachment-97\" class=\"wp-caption-text\">Figure 11. The Gaussian classification software.<\/p><\/div>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":94,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36,2],"tags":[],"class_list":["post-93","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-quantitative-imaging-analysis","category-research"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Image Classification &#8226; Quantitative Bioimaging Laboratory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fei-lab.org\/image-classification\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Image Classification &#8226; Quantitative Bioimaging Laboratory\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fei-lab.org\/image-classification\/\" \/>\n<meta property=\"og:site_name\" content=\"Quantitative Bioimaging Laboratory\" \/>\n<meta property=\"article:published_time\" content=\"2017-03-16T17:33:59+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-28T20:01:32+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2017\/03\/scale_Class_brain_result.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"513\" \/>\n\t<meta property=\"og:image:height\" content=\"318\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"awp-admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"awp-admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/\"},\"author\":{\"name\":\"awp-admin\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#\\\/schema\\\/person\\\/1a52ce2f3ee13559a03a32ac20076dc0\"},\"headline\":\"Image Classification\",\"datePublished\":\"2017-03-16T17:33:59+00:00\",\"dateModified\":\"2023-12-28T20:01:32+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/\"},\"wordCount\":616,\"publisher\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/03\\\/scale_Class_brain_result.jpg\",\"articleSection\":[\"Quantitative Imaging Analysis\",\"Research Topics\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/\",\"name\":\"Image Classification &#8226; Quantitative Bioimaging Laboratory\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/03\\\/scale_Class_brain_result.jpg\",\"datePublished\":\"2017-03-16T17:33:59+00:00\",\"dateModified\":\"2023-12-28T20:01:32+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/fei-lab.org\\\/image-classification\\\/#primaryimage\",\"url\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/03\\\/scale_Class_brain_result.jpg\",\"contentUrl\":\"https:\\\/\\\/fei-lab.org\\\/wp-content\\\/uploads\\\/2017\\\/03\\\/scale_Class_brain_result.jpg\",\"width\":513,\"height\":318,\"caption\":\"Figure 8. 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