Multiparameter Ultrasonic Tissue Characterization and Image Processing: from Experiment to Clinical Application

1996 ◽  
pp. 75-94
Author(s):  
J. M. Thijssen
Keyword(s):  
Image Processing ◽  
Clinical Application ◽  
Tissue Characterization

Tissue characterization by image processing subtraction: Windowing of specific T1 values

1992 ◽  
Vol 10 (6) ◽  
pp. 989-995 ◽  
Author(s):  
S. Bondestam ◽  
A. Lamminen ◽  
M. Komu ◽  
V.-P. Poutanen ◽  
A. Alanen ◽  
...  
Keyword(s):  
Image Processing ◽  
Tissue Characterization

scholarly journals Effects of ceftriaxone on faecal flora: analysis by micromorphometry

1991 ◽  
Vol 106 (3) ◽  
pp. 513-521 ◽  
Author(s):  
B. C. Meijer ◽  
G. J. Kootstra ◽  
D. G. Geertsma ◽  
M. H. F. Wilkinson
Keyword(s):  
Image Processing ◽  
Treatment Period ◽  
Clinical Application ◽  
Digital Image Processing ◽  
Digital Image ◽  
Gut Microflora ◽  
Faecal Flora ◽  
Human Volunteers ◽  
Baseline Values ◽  
Faecal Microflora

SUMMARYIn order to elucidate the effect of ceftriaxone therapy on the morphology of gut microflora, 11 human volunteers were treated with ceftriaxone, 1 g daily, given intramuscularly in one dose. Treatment continued for 5 days. Faecal microflora was analysed by digital image processing before, during and after the treatment period.We derived simple numerical parameters which describe the morphologic composition of the flora. They were significantly influenced by the antibiotic, and returned to their baseline values more than 7 days after treatment was stopped. The procedure holds promise for clinical application.

UTILIZATION OF FRACTAL IMAGE MODELS IN MEDICAL IMAGE PROCESSING

Fractals ◽  
1994 ◽  
Vol 02 (03) ◽  
pp. 363-369 ◽  
Author(s):  
WALTER S. KUKLINSKI
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Fractal Dimension ◽  
Medical Image ◽  
Tissue Characterization ◽  
Optimization Technique ◽  
Medical Image Processing ◽  
Fractal Image ◽  
Segmentation Process ◽  
Image Models

One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimization problem is presented. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

scholarly journals Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251899
Author(s):  
Samir M. Badawy ◽  
Abd El-Naser A. Mohamed ◽  
Alaa A. Hefnawy ◽  
Hassan E. Zidan ◽  
Mohammed T. GadAllah ◽  
...  
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Tissue Characterization ◽  
Semantic Segmentation ◽  
Batch Processing ◽  
Ultrasound Images ◽  
Processing Scheme ◽  
Processing Mode ◽  
Small Dataset

Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

scholarly journals APLICATIVO MÓVEL DE PROCESSAMENTO DE IMAGENS DIGITAIS PARA CLASSIFICAÇÃO AUTOMÁTICA DE TECIDOS DE LESÕES POR PRESSÃO

2020 ◽  
Vol 10 (7) ◽  
Author(s):  
Huara Paiva Castelo Branco ◽  
Levy Aniceto Santana ◽  
Rinaldo De Souza Neves ◽  
Renato Da Veiga Guadagnin
Keyword(s):  
Image Processing ◽  
Pressure Ulcer ◽  
Granulation Tissue ◽  
Mobile Application ◽  
Tissue Characterization ◽  
Controlled Study ◽  
Pressure Injury ◽  
Automated Technique ◽  
Complex Wounds

Objetivo: avaliar o desempenho de uma técnica automática para extração de características dos tipos de tecidos de lesões por pressão por processamento de imagens digitais, embutida em um aplicativo móvel (App) para smartphones. Metodologia: estudo transversal controlado, realizado em 20 imagens de lesões sacrais e trocantéricas. Aferiu-se a concordância na caracterização tecidual presente no leito das lesões entre o App e um comitê de juízes. Resultados: a precisão global do App na identificação de granulação, liquefação e coagulação foi de 75%. Constatou-se independência intraobservador nos desfechos das aferições realizadas pelo aplicativo. Conclusões: o App obteve desfechos promissores ao classificar os tipos de tecidos inviáveis e granulação, sendo necessário aprimoramento do desempenho em feridas complexas e de outras etiologias.Descritores: Lesão por Pressão, Fotografia, Smartphone.MOBILE IMAGING APP FOR AUTOMATIC CLASSIFICATION OF PRESSURE INJURY TISSUESObjective: to evaluate the performance of an automated technique for extraction of characteristics of the types of tissues from pressure lesions by digital image processing, inserted in a mobile application (App) for smartphones. Methodology: crosssectional, controlled study of 20 images of sacral and trochanteric lesions. The concordance in the tissue characterization present in the center of the lesions between the App and a committee of judges was checked. Results: the overall accuracy of the App in the identification of granulation, liquefaction and coagulation was 75%. Intraobserver independence was observed in the results of the measurements performed by the application. Conclusions: the App obtained promising outcomes classifying non-viable tissue types and granulation tissue, and an improvement of the app’s performance is necessary in complex wounds and other etiologies.Descriptores: Pressure Ulcer, Photography, Smartphone.APLICACIÓN MÓVIL DE PROCESAMIENTO DE IMÁGENES DIGITALES PARA LA CLASIFICACIÓN AUTOMÁTICA DE LOS TEJIDOS DE LESIÓN POR PRESIÓNObjetivo: evaluar el rendimiento de una técnica automática para extraer características de los tipos de tejido de las lesiones por presión mediante el procesamiento digital de imágenes, incorporado en una aplicación móvil para smartphone. Metodología: estudio transversal controlado hecho en 20 imágenes de lesiones trocantéricas y en la región sacro. Se verificó la concordancia en la caracterización de los tejidos presentes en el lecho de las lesiones entre la aplicación y un comité de jueces. Resultados: la precisión general de la aplicación en la identificación de tejidos presentes en el lecho de las LPP (lesiones por presión) fue de 75%. Se comprobó la independencia intraobservadora en los puntos finales de las mediciones realizadas por la aplicación. Conclusiones: la aplicación obtuvo resultados promisorios al evaluar los tipos de tejidos no viables y granulación y es necesario prefeccionar el desempeño en heridas complejas y de otras etiologías.Descriptores: Úlcera por Presión, Fotografía, Teléfono Inteligente.

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