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

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.

CALIBRATION OF OPTICAL SILICONE TACTILE SENSOR

2015 ◽  
Vol 76 (8) ◽  
Author(s):  
Muhammad Azmi Ayub ◽  
Nurul Fathiah Mohamed Rosli ◽  
Abdul Halim Esa ◽  
Amir Abdul Latif ◽  
Roseleena Jaafar
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Tissue Characterization ◽  
Tactile Sensor ◽  
Computer Algorithm ◽  
Image Processing Technique ◽  
Image Processing Algorithm ◽  
Study Results ◽  
Analysis Computer ◽  
Sense Of Touch

This paper presents the calibration and development of a computer algorithm to analyze the deformation behavior of the changes in the diameter of a silicone tactile sensor using an image processing technique. In addition, the scope of the work also aims to evaluate the sensor’s sensitivity. Unfortunately, the current design and the system of tactile sensor is not suitable for soft tissue characterization because the sensor system uses multiple optical waveguide transduction technique which is relatively large in diameter size, not flexible and less accurate which is lack of ‘sense of touch’. Hence, an image processing algorithm has been developed using image processing software. The results indicate significant increase in the change in the diameter images. The overall image analysis technique involves the following main stages: image acquisition (capturing of images) and image processing (thresholding, noise filtering, component labeling, and geometric properties). The use of fiber scope and as well as an effective image analysis computer algorithm will facilitate and automate the process for sensing information. This study results in finding the mathematical model of a new technique to establish the sensitivity value of the silicon tactile sensor where a higher sensitivity indicates a more sensitive sensor. The outcomes of this research shows that the functionality of the developed new image analysis computer algorithm technique is suitable to establish the sensing information on the ‘sense of touch’ such as hardness, roughness and other physical characteristics of the surfaces.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

The 'well-known' theory of electron image formation

1983 ◽  
Vol 41 ◽  
pp. 288-289
Author(s):  
M.A. O'Keefe ◽  
W.O. Saxton
Keyword(s):  
Image Processing ◽  
Image Formation ◽  
Source Profiles ◽  
Electron Image

A recent paper by Kirkland on nonlinear electron image processing, referring to a relatively new textbook, highlights the persistence in the literature of calculations based on incomplete and/or incorrect models of electron imageing, notwithstanding the various papers which have recently pointed out the correct forms of the appropriate equations. Since at least part of the problem can be traced to underlying assumptions about the illumination coherence conditions, we attempt to clarify both the assumptions and the corresponding equations in this paper, illustrating the effects of an incorrect theory by means of images calculated in different ways.The first point to be made clear concerning the illumination coherence conditions is that (except for very thin specimens) it is insufficient simply to know the source profiles present, i.e. the ranges of different directions and energies (focus levels) present in the source; we must also know in general whether the various illumination components are coherent or incoherent with respect to one another.

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