Browsing by Subject "Texture analysis"

Now showing 1 - 6 of 6

Open Access
Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution
(SPIE, 2017) Aichinger, W.; Krappe, S.; Çetin, A. Enis; Çetin-Atalay, R.; Üner, A.; Benz, M.; Wittenberg, T.; Stamminger, M.; Münzenmayer, C.
The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our work we aim at identifying CSC presence on ubiquitous Hematoxilyn and Eosin (HE) staining as an inexpensive tool for routine histopathology based on their distinct morphological features. We present initial results of a new method based on color deconvolution (CD) and convolutional neural networks (CNN). This method performs favorably (accuracy 0.936) in comparison with a state-of-the-art method based on 1DSIFT and eigen-analysis feature sets evaluated on the same image database. We also show that accuracy of the CNN is improved by the CD pre-processing.
Open Access
Automatic detection and segmentation of orchards using very high resolution imagery
(Institute of Electrical and Electronics Engineers, 2012-08) Aksoy, S.; Yalniz, I. Z.; Tasdemir, K.
Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multi-granularity isotropic filters. Then, the regularity of the planting patterns is quantified using projection profiles of the filter responses at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data. © 2012 IEEE.
Open Access
Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data
(2007) Woods, B.J.; Clymer, B.D.; Kurc, T.; Heverhagen J.T.; Stevens, R.; Orsdemir, A.; Bulan O.; Knopp, M.V.
Purpose: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods: 4D texture analysis was performedon DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with α = 0.05. The results show that an area under the ROC curve (Az) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted. © 2007 Wiley-Liss, Inc.
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
Unsupervised detection of compound structures using image segmentation and graph-based texture analysis
(2009) Zamalieva, Daniya
The common goal of object-based image analysis techniques in the literature is to partition the images into homogeneous regions and classify these regions. However, such homogeneous regions often correspond to very small details in very high spatial resolution images obtained from the new generation sensors. One interesting way of enabling the high-level understanding of the image content is to identify the image regions that are intrinsically heterogeneous. These image regions are comprised of primitive objects of many diverse types, and can also be referred to as compound structures. The detection of compound structures can be posed as a generalized segmentation or generalized texture detection problem, where the elements of interest are primitive objects instead of traditional case of pixels. Traditional segmentation methods extract regions with similar spectral content and texture models assume specific scale and orientation. Hence, they cannot handle the complexity of compound structures that consist of multiple regions with different spectral content and arbitrary scale and orientation. In this thesis, we present an unsupervised method for discovering compound image structures that are comprised of simpler primitive objects. An initial segmentation step produces image regions with homogeneous spectral content. Then, the segmentation is translated into a relational graph structure whose nodes correspond to the regions and the edges represent the relationships between these regions. We assume that the region objects that appear together frequently can be considered as strongly related. This relation is modeled using the transition frequencies between neighboring regions, and the significant relations are found as the modes of a probability distribution estimated using the features of these transitions. Furthermore, we expect that subgraphs that consist of groups of strongly related regions correspond to compound structures. Therefore, we employ two different procedures to discover the subgraphs in the constructed graph. During the first procedure the graph is discretized and a graph-based knowledge discovery algorithm is applied to find the repeating subgraphs. Even though a single subgraph does not exclusively correspond to a particular compound structure, different subgraphs constitute parts of different compound structures. Hence, we discover compound structures by clustering the histograms of the subgraph instances with sliding image windows. The second procedure involves graph segmentation by using normalized cuts. Since the distribution of significant relations within resulting subgraphs gives an idea about the nature of corresponding compound structure, the subgraphs are further grouped by clustering the histograms of the most significant relations. The proposed method was tested using an Ikonos image. Experiments show that the discovered image areas correspond to different high-level structures with heterogeneous content such as dense residential areas with high buildings, dense and sparse residential areas with low height buildings and fields.
Open Access
Unsupervised tissue image segmentation through object-oriented texture
(IEEE, 2010) Tosun, Akif Burak; Sokmensuer, C.; Gündüz-Demir, Çiğdem
This paper presents a new algorithm for the unsupervised segmentation of tissue images. It relies on using the spatial information of cytological tissue components. As opposed to the previous study, it does not only use this information in defining its homogeneity measures, but it also uses it in its region growing process. This algorithm has been implemented and tested. Its visual and quantitative results are compared with the previous study. The results show that the proposed segmentation algorithm is more robust in giving better accuracies with less number of segmented regions. © 2010 IEEE.