Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

Coelho, Luís Pedro; Glory-Afshar, Estelle; Kangas, Joshua; Quinn, Shannon; Shariff, Aabid; Murphy, Robert F.

doi:10.1007/978-3-642-13131-8_2

Cited by 10 publications

(9 citation statements)

References 49 publications

(46 reference statements)

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“…Nevertheless, specific imaging contexts, such as bioimage processing, need textural descriptors able to reflect their peculiar characteristics and challenges [10].…”

Section: Texture Analysis: Definition and Main Application Areasmentioning

confidence: 99%

Mining textural knowledge in biological images: Applications, methods and trends

Cataldo

Ficarra

2017

Computational and Structural Biotechnology Journal

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Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this paper, we perform a critical review of this important topic. First, we provide a general definition of texture analysis and discuss its role in the context of bioimaging, with examples of applications from the recent literature. Then, we review the main approaches to automated texture analysis, with special attention to the methods of feature extraction and encoding that can be successfully applied to microscopy images of cells or tissues. Our aim is to provide an overview of the state of the art, as well as a glimpse into the latest and future trends of research in this area.

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“…Nevertheless, specific imaging contexts, such as bioimage processing, need textural descriptors able to reflect their peculiar characteristics and challenges [10].…”

Section: Texture Analysis: Definition and Main Application Areasmentioning

confidence: 99%

Mining textural knowledge in biological images: Applications, methods and trends

Cataldo

Ficarra

2017

Computational and Structural Biotechnology Journal

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“…There is very little burn image classification systems suggested by some researchers [4,9,12]. For example, M. Survana has applied Template Matching, k-NN and SVM classification methods for skin burn images with their own collection dataset with only 120 images in 3 types of burns (superficial dermal, partial thickness and full thickness).…”

Section: Classifier Based On Machine Learningmentioning

confidence: 99%

Real Time Burning Image Classification Using Support Vector Machine

Hai¹,

Triet²,

Thai³

et al. 2017

EAI Endorsed Transactions on Context-aware Systems and Applicat

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Burning image classification is critical and attempted problems in medical image processing. This paper has proposed the real time image classification for burning image to automatically identify the degrees of burns in three levels: II, III, and IV. The proposed model uses the multi-colour channels extraction and binary based on adaptive threshold. The proposed model uses One-class Support Vector Machine instead of traditional Support Vector Machine (SVM) because of unbalanced degrees of burns images database. The classifying precision 77.78% shows the feasibility of our proposed model.

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“…Moreover, such a tool has been used in medical image registrations. Thus, its application to biology has garnered considerable attention in biology [58,14,50].…”

Section: Organellar Classifications Via Svm With Hlacmentioning

confidence: 99%

Communication Platform for Image Analysis and Sharing in Biology

Murakami

Tawara

Nishimura

et al. 2014

IJNC

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Image analysis is crucial to medical and biological applications. Recent advances in imaging technology have led to the demand for processing and visualizing a large amount of three-dimensional (3D) biomedical images. In addition, cloud computing has become popular for managing big data. Unfortunately, conventional image-processing systems either lack cloud computing services or advanced 3D processing abilities. In this paper, we present a novel cloud-based system for sharing, processing, and visualizing 3D biomedical images. Our system employs a standard web browser as a client interface that interactively communicates with high-performance servers. Thus, an inexpensive tablet PC without an advanced graphics processing unit (GPU) can be used for 3D image processing and visualization. Our system provides the sharing of limited software and hardware resources, and it allows for effective collaboration between researchers. We demonstrate the applicability and functionality of the system by examining typical case studies on biomedical images. We also examine the performance of our system numerically.

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Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

Cited by 10 publications

References 49 publications

Mining textural knowledge in biological images: Applications, methods and trends

Mining textural knowledge in biological images: Applications, methods and trends

Real Time Burning Image Classification Using Support Vector Machine

Communication Platform for Image Analysis and Sharing in Biology

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