Automated Disease Classification of Colon and Gastric Histological Samples Based on Digital Microscopy and Advanced Image Analysis

2008 ◽  
pp. 99-111
Author(s):  
Levente Ficsor ◽  
Bela Molnar
Keyword(s):  
Image Analysis ◽  
Disease Classification ◽  
Digital Microscopy

scholarly journals Automated classification of inflammation in colon histological sections based on digital microscopy and advanced image analysis

2008 ◽  
Vol 73A (3) ◽  
pp. 230-237 ◽  
Author(s):  
Levente Ficsor ◽  
Viktor Sebestyén Varga ◽  
Attila Tagscherer ◽  
Zsolt Tulassay ◽  
Bela Molnar
Keyword(s):  
Image Analysis ◽  
Digital Microscopy ◽  
Automated Classification ◽  
Histological Sections

Colour image analysis for the classification of turkey breast meat according to cooking yield

2003 ◽  
Vol 23 (1) ◽  
pp. 124-127
Author(s):  
Isabel Sebastáan ◽  
V Santé ◽  
G Le Pottier ◽  
Pascale Marty-Mahé ◽  
P Loisel ◽  
...  
Keyword(s):  
Image Analysis ◽  
Breast Meat ◽  
Colour Image ◽  
Cooking Yield

scholarly journals Classification of endonasal HHT lesions using digital microscopy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
F. Haubner ◽  
A. Schneider ◽  
H. Schinke ◽  
M. Bertlich ◽  
B. G. Weiss ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Treatment Modalities ◽  
Hereditary Haemorrhagic Telangiectasia ◽  
Digital Microscopy ◽  
Digital Microscope ◽  
Lower Severity ◽  
Gender Specific Differences ◽  
Vegf Level

Abstract Background Recurrent spontaneous epistaxis is the most common clinical manifestation and the most debilitating symptom in hereditary haemorrhagic telangiectasia (HHT) patients. To this date, there exist only a classification of HHT patients by different genetic mutations. There is no standard classification for the mucocutaneous endonasal manifestations of HHT. The aim of the present study was to document the variety of endonasal HHT lesions using digital microscopy and to propose a clinical classification. Methods We recorded the endonasal HHT lesions of 28 patients using a digital microscope. We reconstructed the 3D images und videos recorded by digital microscope afterwards and classified the endonasal lesions of HHT in two classes: Grade A, presence of only flat telangiectasias in the mucosa level and Grade B, (additional) presence of raised berry or wart-like telangiectasia spots. We investigated also Haemoglobin level by routine laboratory procedures, plasma VEGF level by ELISA, Severity of epistaxis by epistaxis severity score (ESS) and quality of life by a linear visual analogue scale (VAS). Results We found a higher quality of life and a lower severity of epistaxis in Grade A patients in comparison to Grade B patients. No difference in plasma VEGF level and in Haemoglobin between Grad A patients and Grade B patients could be detected. Plasma VEGF levels showed no gender specific differences. It could also not be correlated to the extranasal manifestation. Conclusion The classification for endonasal manifestation of HHT proposed in this study indicates severity of epistaxis und quality of life. Digital microscopy with the ability of 3D reconstruction of images presents a useful tool for such classifications. The classification of endonasal HHT lesions using digital microscopy allows to evaluate the dynamic of HHT lesions in the course of time independent of examiner. This allows also to evaluate the efficacy of the different treatment modalities by dynamic of HHT lesions. Moreover digital microscopy is very beneficial in academic teaching of rare diseases.

scholarly journals Classification of total carotene and quality of chili pepper (Capsicum frutescens) based on image analysis

2021 ◽  
Vol 733 (1) ◽  
pp. 012005
Author(s):  
Y Hendrawan ◽  
R Utami ◽  
D Y Nurseta ◽  
Daisy ◽  
S Nuryani ◽  
...  
Keyword(s):  
Image Analysis ◽  
Chili Pepper ◽  
Capsicum Frutescens

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

Dimensionality Reduction and Classification of Hyperspectral Images Using Object-Based Image Analysis

2018 ◽  
Vol 46 (8) ◽  
pp. 1297-1306 ◽  
Author(s):  
Taskin Kavzoglu ◽  
Hasan Tonbul ◽  
Merve Yildiz Erdemir ◽  
Ismail Colkesen
Keyword(s):  
Image Analysis ◽  
Dimensionality Reduction ◽  
Hyperspectral Images ◽  
Object Based Image Analysis ◽  
Object Based
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