Browsing by Subject "Histopathological image representation"

Now showing 1 - 3 of 3

Open Access
Two-tier tissue decomposition for histopathological image representation and classification
(2014) Gültekin, Tunç
In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly define their objects, based on the color information, as to approximately represent histological tissue components in an image and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this thesis, we present a new model for effective representation and classification of histopathological images. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining the texture, shape, and size information and they may correspond to individual histological components as well as tissue sub-regions of different characteristics. As its second contribution, it defines a new metric, which we call “dominant blob scale”, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.
Open Access
Two-tier tissue decomposition for histopathological image representation and classification
(Institute of Electrical and Electronics Engineers, 2015) Gultekin, T.; Koyuncu, C. F.; Sokmensuer, C.; Gunduz Demir, C.
In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.
Open Access
Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images
(Institute of Electrical and Electronics Engineers, 2019) Sarı, Can Taylan; Gündüz-Demir, Çiğdem
Histopathological examination is today’s gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly rely on features that they use, and thus, their success strictly depends on the ability of these features successfully quantifying the histopathology domain. With this motivation, this paper presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This feature extractor has three main contributions: First, it proposes to identify salient subregions in an image, based on domain-specific prior knowledge, and to quantify the image by employing only the characteristics of these subregions instead of considering the characteristics of all image locations. Second, it introduces a new deep learning based technique that quantizes the salient subregions by extracting a set of features directly learned on image data and uses the distribution of these quantizations for image representation and classification. To this end, the proposed deep learning based technique constructs a deep belief network of restricted Boltzmann machines (RBMs), defines the activation values of the hidden unit nodes in the final RBM as the features, and learns the quantizations by clustering these features in an unsupervised way. Third, this extractor is the first example of successfully using restricted Boltzmann machines in the domain of histopathological image analysis. Our experiments on microscopic colon tissue images reveal that the proposed feature extractor is effective to obtain more accurate classification results compared to its counterparts. IEEE