Browsing by Subject "Diagnostic imaging--Digital techniques."

Now showing 1 - 8 of 8

Open Access
Color graph representation for structural analysis of tissue images
(2010) Altunbay, Doğan
Computer aided image analysis tools are becoming increasingly important in automated cancer diagnosis and grading. They have the potential of assisting pathologists in histopathological examination of tissues, which may lead to a considerable amount of subjectivity. These analysis tools help reduce the subjectivity, providing quantitative information about tissues. In literature, it has been proposed to implement such computational tools using different methods that represent a tissue with different set of image features. One of the most commonly used methods is the structural method that represents a tissue quantifying the spatial relationship of its components. Although previous structural methods lead to promising results for different tissue types, they only use the spatial relations of nuclear tissue components without considering the existence of different components in a tissue. However, additional information that could be obtained from other components of the tissue has an importance in better representing the tissue, and thus, in making more reliable decisions. This thesis introduces a novel structural method to quantify histopathological images for automated cancer diagnosis and grading. Unlike the previous structural methods, it proposes to represent a tissue considering the spatial distribution of different tissue components. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their end points. Subsequently, a new set of structural features is extracted from these “color graphs” and used in the classification of tissues. Experiments conducted on 3236 photomicrographs of colon tissues that are taken from 258 different patients demonstrate that the color graph approach leads to 94.89 percent training accuracy and 88.63 percent test accuracy. Our experiments also show that the introduction of color edges to represent the spatial relationship of different tissue components and the use of graph features defined on these color edges significantly improve the classification accuracy of the previous structural methods.
Open Access
Histopathological image classification using salient point patterns
(2011) Çığır, Celal
Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer valuable opportunities to reduce and eliminate the inter- and intra-observer variations in diagnosis, which is very common in the current practice of histopathological examination. Many studies have been dedicated to develop such systems for cancer diagnosis and grading, especially based on textural and structural tissue image analysis. Although the recent textural and structural approaches yield promising results for different types of tissues, they are still unable to make use of the potential biological information carried by different tissue components. However, these tissue components help better represent a tissue, and hence, they help better quantify the tissue changes caused by cancer. This thesis introduces a new textural approach, called Salient Point Patterns (SPP), for the utilization of tissue components in order to represent colon biopsy images. This textural approach first defines a set of salient points that correspond to nuclear, stromal, and luminal components of a colon tissue. Then, it extracts some features around these salient points to quantify the images. Finally, it classifies the tissue samples by using the extracted features. Working with 3236 colon biopsy samples that are taken from 258 different patients, our experiments demonstrate that Salient Point Patterns approach improves the classification accuracy, compared to its counterparts, which do not make use of tissue components in defining their texture descriptors. These experiments also show that different set of features can be used within the SPP approach for better representation of a tissue image.
Open Access
Image processing methods for computer-aided interpretation of microscopic images
(2012) Keskin, Musa Furkan
Image processing algorithms for automated analysis of microscopic images have become increasingly popular in the last decade with the remarkable growth in computational power. The advent of high-throughput scanning devices allows for computer-assisted evaluation of microscopic images, resulting in a quick and unbiased image interpretation that will facilitate the clinical decision-making process. In this thesis, new methods are proposed to provide solution to two image analysis problems in biology and histopathology. The first problem is the classification of human carcinoma cell line images. Cancer cell lines are widely used for research purposes in laboratories all over the world. In molecular biology studies, researchers deal with a large number of specimens whose identity have to be checked at various points in time. A novel computerized method is presented for cancer cell line image classification. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DTCWT) coefficients as pixel features is computed. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. For 14 different classes, we achieve an overall accuracy of 98%, which outperforms the classical covariance based methods. Histopathological image analysis problem is related to the grading of follicular lymphoma (FL) disease. FL is one of the commonly encountered cancer types in the lymph system. FL grading is based on histological examination of hematoxilin and eosin (H&E) stained tissue sections by pathologists who make clinical decisions by manually counting the malignant centroblast (CB) cells. This grading method is subject to substantial inter- and intra-reader variability and sampling bias. A computer-assisted method is presented for detection of CB cells in H&Estained FL tissue samples. The proposed algorithm takes advantage of the scalespace representation of FL images to detect blob-like cell regions which reside in the scale-space extrema of the difference-of-Gaussian images. Multi-stage false positive elimination strategy is employed with some statistical region properties and textural features such as gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM) and Scale Invariant Feature Transform (SIFT). The algorithm is evaluated on 30 images and 90% CB detection accuracy is obtained, which outperforms the average accuracy of expert hematopathologists.
Open Access
Local object patterns for tissue image representation and cancer classification
(2013) Olgun, Gülden
Histopathological examination of a tissue is the routine practice for diagnosis and grading of cancer. However, this examination is subjective since it requires visual interpretation of a pathologist, which mainly depends on his/her experience and expertise. In order to minimize the subjectivity level, it has been proposed to use automated cancer diagnosis and grading systems that represent a tissue image with quantitative features and use these features for classifying and grading the tissue. In this thesis, we present a new approach for effective representation and classification of histopathological tissue images. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns. However, we define our local object pattern descriptors at the component-level to quantify a component, as opposed to pixel-level local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. In this thesis, we use two approaches for the selection of the components: The first approach uses all components to construct a bag-ofwords representation whereas the second one uses graph walking to select multiple subsets of the components and constructs multiple bag-of-words representations from these subsets. Working with microscopic images of histopathological colon tissues, our experiments show that the proposed component-level texture descriptors lead to higher classification accuracies than the previous textural approaches.
Open Access
Multilevel cluster ensembling for histopathological image segmentation
(2011) Şimşek, Ahmet Çağrı
In cancer diagnosis and grading, histopathological examination of tissues by pathologists is accepted as the gold standard. However, this procedure has observer variability and leads to subjectivity in diagnosis. In order to overcome such problems, computational methods which use quantitative measures are proposed. These methods extract mathematical features from tissue images assuming they are composed of homogeneous regions and classify images. This assumption is not always true and segmentation of images before classification is necessary. There are methods to segment images but most of them are proposed for generic images and work on the pixel-level. Recently few algorithms incorporated medical background knowledge into segmentation. Their high level feature definitions are very promising. However, in the segmentation step, they use region growing approaches which are not very stable and may lead to local optima. In this thesis, we present an efficient and stable method for the segmentation of histopathological images which produces high quality results. We use existing high level feature definitions to segment tissue images. Our segmentation method significantly improves the segmentation accuracy and stability, compared to existing methods which use the same feature definition. We tackle image segmentation problem as a clustering problem. To improve the quality and the stability of the clustering results, we combine different clustering solutions. This approach is also known as cluster ensembles. We formulate the clustering problem as a graph partitioning problem. In order to obtain diverse and high quality clustering results quickly, we made modifications and improvements on the well-known multilevel graph partitioning scheme. Our method clusters medically meaningful components in tissue images into regions and obtains the final segmentation. Experiments showed that our multilevel cluster ensembling approach performed significantly better than existing segmentation algorithms used for generic and tissue images. Although most of the images used in experiments, contain noise and artifacts, the proposed algorithm produced high quality results.
Open Access
Novel methods for microscopic image processing, analysis, classification and compression
(2013) Suhre, Alexander
Microscopic images are frequently used in medicine and molecular biology. Many interesting image processing problems arise after the initial data acquisition step, since image modalities are manifold. In this thesis, we developed several algorithms in order to handle the critical pipeline of microscopic image storage/ compression and analysis/classification more efficiently. The first step in our processing pipeline is image compression. Microscopic images are large in size (e.g. 100K-by-100K pixels), therefore finding efficient ways of compressing such data is necessary for efficient transmission, storage and evaluation. We propose an image compression scheme that uses the color content of a given image, by applying a block-adaptive color transform. Microscopic images of tissues have a very specific color palette due to the staining process they undergo before data acquisition. The proposed color transform takes advantage of this fact and can be incorporated into widely-used compression algorithms such as JPEG and JPEG 2000 without creating any overhead at the receiver due to its DPCM-like structure. We obtained peak signal-to-noise ratio gains up to 0.5 dB when comparing our method with standard JPEG. The next step in our processing pipeline is image analysis. Microscopic image processing techniques can assist in making grading and diagnosis of images reproducible and by providing useful quantitative measures for computer-aided diagnosis. To this end, we developed several novel techniques for efficient feature extraction and classification of microscopic images. We use region co-difference matrices as inputs for the classifier, which have the main advantage of yielding multiplication-free computationally efficient algorithms. The merit of the co-difference framework for performing some important tasks in signal processing is discussed. We also introduce several methods that estimate underlying probability density functions from data. We use sparsity criteria in the Fourier domain to arrive at efficient estimates. The proposed methods can be used for classification in Bayesian frameworks. We evaluated the performance of our algorithms for two image classification problems: Discriminating between different grades of follicular lymphoma, a medical condition of the lymph system, as well as differentiating several cancer cell lines from each another. Classification accuracies over two large data sets (270 images for follicular lymphoma and 280 images for cancer cell lines) were above 98%.
Open Access
Object-oriented testure analysis and unsupervised segmentation for histopathological images
(2012) Tosun, Akif Burak
The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. The segmentation algorithms in literature commonly use pixel-level color/texture descriptors that they define on image pixels for quantizing a tissue. On the other hand, it is usually harder to express domain specific knowledge about tissues, such as the spatial organization of tissue components, using only the pixel-level descriptors. This may become even harder for tissue images, which typically consist of a considerable amount of variation and noise at their pixel-level, such as similar color distribution of different tissue components, distortion in cell alignments, and color contrast caused by too much stain in a particular region. The previous segmentation algorithms are more susceptible to these problems as they work on pixel-level descriptors. In order to successfully address these issues, in this thesis, we introduce three new texture descriptors, namely ObjSEG, GraphRLM, and ObjCooc textures, and implement algorithms that use these descriptors for segmenting histopathological tissue images. We extract these texture descriptors on tissue components that are approximately represented by circular objects. Since these objectoriented texture descriptors are defined on the tissue components, and hence domain specific knowledge, they represent the spatial organization of the components better than their previous counterparts. Thus, our algorithms based on these descriptors give more effective and robust segmentation results. Furthermore, since the descriptors are not directly defined on image pixels, they are effective to alleviate the pixel-level problems. In our experiments, we tested our algorithms that use the proposed objectoriented descriptors on a dataset of 200 colon tissue images. Our experiments demonstrated that our new object-oriented feature descriptors led to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with its previous counterparts, the experimental results also showed that our proposed algorithms are more effective in segmenting histopathological images.
Open Access
Resampling-based Markovian modeling for automated cancer diagnosis
(2011) Özdemir, Erdem
Correct diagnosis and grading of cancer is very crucial for planning an effective treatment. However, cancer diagnosis on biopsy images involves visual interpretation of a pathologist, which is highly subjective. This subjectivity may, however, lead to selecting suboptimal treatment plans. In order to circumvent this problem, it has been proposed to use automatic diagnosis and grading systems that help decrease the subjectivity levels by providing quantitative measures. However, one major challenge for designing these systems is the existence of high variance observed in the biopsy images due to the nature of biopsies. Thus, for successful classifications of unseen images, these systems should be trained with a large number of labeled images. However, most of the training sets in this domain have limited size of labeled data since it is quite difficult to collect and label histopathological images. In this thesis, we successfully address this issue by presenting a new resampling framework. This framework relies on increasing the generalization capacity of a classifier by augmenting the size and variation in the training set. To this end, we generate multiple sequences from an image, each of which corresponds to a perturbed sample of the image. Each perturbed sample characterizes different parts of the image, and hence, they are slightly different from each other. The use of these perturbed samples for representing the image increases the size and variability of the training set. These samples are modeled with Markov processes which are used to classify unseen image. Working with histopathological tissue images, our experiments demonstrate that the proposed framework is more effective for both larger and smaller training sets compared against other approaches. Additionally, they show that the use of perturbed samples is effective in a voting scheme which boosts the performance of the classifier.