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The Vectorial Minimum Barrier Distance
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Department of Electrical and Computer Engineering and the Department of Radiology, The University of Iowa, Iowa City, IA 52242 USA.
2012 (engelsk)Inngår i: International Conference on Pattern Recognition, ISSN 1051-4651, s. 792-795Artikkel i tidsskrift, Meeting abstract (Fagfellevurdert) Published
Abstract [en]

We introduce the vectorial Minimum Barrier Distance (MBD), a method for computing a gray-weighted distance transform while also incorporating information from vectorial data. Compared to other similar tools that use vectorial data, the proposed method requires no training and does not assume having only one background class. We describe a region growing algorithm for computing the vectorial MBD efficiently.

The method is evaluated on two types of multi-channel images: color images and textural features. Different path-cost functions for calculating the multi-dimensional path-cost distance are also compared.

The results show that by combining multi-channel images into vectorial information the performance ofthe vectorial MBD segmentation is improved compared to when one channel is used. This implies that the method can be a good way of incorporating multi-channel information in interactive segmentation.

sted, utgiver, år, opplag, sider
2012. s. 792-795
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-190013ISBN: 978-1-4673-2216-4 (tryckt)OAI: oai:DiVA.org:uu-190013DiVA, id: diva2:582815
Konferanse
International Conference on Pattern Recognition, 2012
Tilgjengelig fra: 2013-01-07 Laget: 2013-01-07 Sist oppdatert: 2014-04-29bibliografisk kontrollert
Inngår i avhandling
1. Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis
Åpne denne publikasjonen i ny fane eller vindu >>Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation.  In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2014. s. 129
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1128
Emneord
image analysis, breast cancer diagnosis, digital histopathology, immunohistochemistry, biomarker quantification
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-219306 (URN)978-91-554-8889-5 (ISBN)
Disputas
2014-04-11, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-03-20 Laget: 2014-02-26 Sist oppdatert: 2014-07-21

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