Multiregional Segmentation Modeling in Medical Ultrasonography: Extraction, Modeling and Quantification of Skin Layers and Hypertrophic Scars

Bryjova, Iveta; Kubíček, Jan; Molnarova, Kristyna; Peter, Lukas; Penhaker, Marek; Kuča, Kamil

doi:10.1007/978-3-319-67077-5_18

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Due to the fact that the segmentation algorithms are among the most explored in medical applications and the universality of their solutions enables their application in various areas, the HFUS image segmentation methods, among other CAD solutions, were historically first addressed in the literature in [ 27 , 64 ]. At the same, they are the most widely described and evaluated algorithms [ 10 , 14 , 26 , 27 , 63 , 64 , 92 ].…”

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

“…The extraction, modeling, and quantification of skin layers was the target of the work [ 92 ] by Bryjova et al The segmentation method utilized a mathematical model of skin morphology based on the skin layer skeleton, and the study aimed to assess burn treatment. To verify the segmentation method, the authors used a Mindray M7, Mindray (Shenzhen, China) machine with an 11 MHz transducer.…”

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

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Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Czajkowska

Borak

2022

Sensors

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Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.

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Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Czajkowska

Borak

2022

Sensors

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“…Considering the concept of transfer learning, we used a pre-trained Inception V3 model [ 37 ], which is powerful enough for feature extraction. Our DNN can be described as [ 25 , 38 ], which means there is a layer of 10 neurons, where each is connected to 20 neurons in the next layer, and similarly each is connected to 10 neurons in the third layer. In addition, we retrained the final classified layer so that it could determine cancer versus no cancer with considerable confidence (>0.6).…”

Section: Proposed Modelmentioning

confidence: 99%

Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model

Mambou

Marešová

Krejcar

et al. 2018

Sensors

Self Cite

215

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Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.

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Design and Analysis of LMMSE Filter for MR Image Data

Kubíček

Krestanova

Polachova

et al. 2019

Intelligent Information and Database Systems

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Multiregional Segmentation Modeling in Medical Ultrasonography: Extraction, Modeling and Quantification of Skin Layers and Hypertrophic Scars

Cited by 4 publications

References 15 publications

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model

Design and Analysis of LMMSE Filter for MR Image Data

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