Using immunoadjuvant agent glycated chitosan to enhance anti-cancer stem like cell immunity induced by HIFU

Chen, Y.-L.; Chen, W.-R.; Liu, R.-S.; Yang, Feng; Wang, C.-Y.; Lee, Y.-J.

doi:10.1117/12.2002722

Cited by 1 publication

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“…Since 40 years of its applications, it has become an important basis for the analysis and diagnosis of clinical pathology and widely used in markers of angiogenesis [6], the human protein atlas [26], atherosclerosis progression [20], the dynamics of skin inflammation [23], pulmonary nodules [33], and COPD [35], particularly plays an important role in tumor/cancer qualitative, quantitative, classification, prognosis and diagnosis [1,4,5,8,11,12,18,32,43,44]. Scholars such as Irshad et al [13,14] took immunohistochemistry as key and gave the development direction in digital processing.…”

Section: Introductionmentioning

confidence: 99%

Extraction of target region in lung immunohistochemical image based on artificial neural network

Fan

Cao

2016

Multimed Tools Appl

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Immunohistochemistry is widely used in clinical pathology analysis and diagnosis, the target regions segmentation is the key procedure and always provides support for many qualitative and quantitative analyses on digitized immunohistochemical image. In lung tissue immunohistochemistry applications, the target region needs to be extracted out of the whole image firstly. Most existing methods based on color cannot fulfill the extraction of antibody region. Thus, there is a need of effective extraction method. Methods: According to the features of target region in images to be processed, this paper presents a solution framework based on artificial neural network (ANN). Results: Six effective features of the candidate regions are analyzed and extracted as the inputs of the ANN; three-layers back propagation neural network with six inputs and one output is constructed, and ANN's parameters are trained by the learning image set. By the trained ANN, target region core are obtained and then expanded to the whole target region through conditional expansion. Conclusion: Through testing the framework by testing image set and comparing with the main existing methods, it can be concluded that the proposed framework can remove non-target regions and extract the target regions well, while the contrast methods cannot remove all the non-target regions. Significance: The method presented in this paper has practical and potential significance to realize automated and quantitative tissue immunohistochemical image analysis.

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