Automatic Extraction of Soft Tissue Tumor from Ultrasonography Using ART2 Based Intelligent Image Analysis

Park, Joonsung; Song, Doo Heon; Han, Sang Suk; Kim, Kwang-Baek

doi:10.2174/1573405613666170504153002

Cited by 7 publications

(9 citation statements)

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“…Since the articulated point of the proposed method lies in the new fuzzy rule-based stretching, we compare the effect of the proposed stretching with the previous approach used in [24], as shown in Figure 9, to demonstrate its better contrast. In the quantization process, we used FCM, as demonstrated in Figure 6.…”

Section: Resultsmentioning

confidence: 99%

“…However, there is another alternative for intelligent quantization-ART2 learning. ART2 is also an unsupervised real-time stable learning algorithm that does not suffer from the local minima, and it was very successful in addressing the automatic soft tissue extraction problem [24]. We compare the proposed FCM with the ART2 applied in [24], as shown in Figure 10.…”

Section: Resultsmentioning

confidence: 99%

“…Image enhancement is a process of converting the visual appearance of the image into a better image compared with the original image. It is usually used as a support for better analysis results [24,25]. Furthermore, in many cases of such human organ ultrasonography, the area of the target organ is often too dark, meaning that there can be important information loss after binarization, which may affect later object forming processes.…”

Section: Fuzzy Stretching With Trapezoid Membership Function In Image Enhancementmentioning

confidence: 99%

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Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

2021

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Ganglion cysts are commonly observed in association with the joints and tendons of the appendicular skeleton. Ultrasonography is the favored modality used to manage such benign tumors, but it may suffer from operator subjectivity. In the treatment phase, ultrasonography also provides guidance for aspiration and injection, and the information regarding the accurate location of the pedicle of the ganglion. Thus, in this paper, we propose an automatic ganglion cyst extracting method based on fuzzy stretching and fuzzy C-means quantization. The proposed method, with its carefully designed image-enhancement policy, successfully detects ganglion cysts in 86 out of 90 cases (95.6%) without requiring human intervention.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Fuzzy Stretching With Trapezoid Membership Function In Image Enhancementmentioning

confidence: 99%

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Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

2021

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“…Although the FFT can effectively remove noise at the microvessel boundaries and background areas via a high-pass filter, the entire background area cannot be removed. Therefore, the ART2-based quantization technique was employed to extract the candidate background areas [16]. The ART2 is an unsupervised learning algorithm that builds clusters of similar features by autonomously learning the input patterns.…”

Section: Removal Of the Background Area Using The Art2 Algorithmmentioning

confidence: 99%

Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

Kim

et al. 2020

Applied Sciences

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Predicting the depth of invasion of superficial esophageal squamous cell carcinomas (SESCCs) is important when selecting treatment modalities such as endoscopic or surgical resections. Recently, the Japanese Esophageal Society (JES) proposed a new simplified classification for magnifying endoscopy findings of SESCCs to predict the depth of tumor invasion based on intrapapillary capillary loops with the SESCC microvessels classified into the B1, B2, and B3 types. In this study, a four-step classification method for SESCCs is proposed. First, Niblack’s method was applied to endoscopy images to select a candidate region of microvessels. Second, the background regions were delineated from the vessel area using the high-speed fast Fourier transform and adaptive resonance theory 2 algorithm. Third, the morphological characteristics of the vessels were extracted. Based on the extracted features, the support vector machine algorithm was employed to classify the microvessels into the B1 and non-B1 types. Finally, following the automatic measurement of the microvessel caliber using the proposed method, the non-B1 types were sub-classified into the B2 and B3 types via comparisons with the caliber of the surrounding microvessels. In the experiments, 114 magnifying endoscopy images (47 B1-type, 48 B2-type, and 19 B3-type images) were used to classify the characteristics of SESCCs. The accuracy, sensitivity, and specificity of the classification into the B1 and non-B1 types were 83.3%, 74.5%, and 89.6%, respectively, while those for the classification of the B2 and B3 types in the non-B1 types were 73.1%, 73.7%, and 72.9%, respectively. The proposed machine learning based computer-aided diagnostic system could obtain the objective data by analyzing the pattern and caliber of the microvessels with acceptable performance. Further studies are necessary to carefully validate the clinical utility of the proposed system.

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“…Automatic extraction or segmentation of a human organ for the reliable diagnosis from US is a solution for the US operator subjectivity problem. There have been several successful attempts of developing such automatic tools in soft tissue tumor extraction [13,14], ganglion cyst detection [15], muscle extractions related to cervical vertebrae [16,17], measuring carotid artery Intima-Media Thickness [18], and detecting breast cancer cells [19]. Like many other medical diagnosis, there are many known measurement indices to give a reliable diagnosis of acute appendicitis from sonographic findings such as outer appendiceal diameter, lack of compressibility, intraluminal fluid, visualization of appendicolith, increased color signals along its wall, cecal wall thickening, periileal lymph nodes, and peritoneal fluid [11,20].…”

Section: Introductionmentioning

confidence: 99%

Semi-dynamic Control of FCM Initialization for Automatic Extraction of Inflamed Appendix from Ultrasonography

Kim

Park

Song

2019

CMIR

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Background: Current naked-eye examination of the ultrasound images for inflamed appendix has limitations due to its intrinsic operator subjectivity problem. Objective: In this paper, we propose a fully automatic intelligent method for extracting inflamed appendix from ultrasound images. Accurate and automatic extraction of inflamed appendix from ultrasonography is a major decision making resource of the diagnosis and management of suspected appendicitis. Methods: The proposed method uses Fuzzy C-means learning algorithm in pixel clustering with semi-dynamic control of initializing the number of clusters based on the intensity contrast dispersion of the input image. Thirty percent of the prepared ultrasonography samples are classified into four different groups based on their intensity contrast distribution and then different number of clusters are assigned to the images in accordance with such groups in Fuzzy C-means learning process. Results: In the experiment, the proposed system successfully extracts the target without human intervention in 82 of 85 cases (96.47% accuracy). The proposed method also shows that it can cover the false negative cases occurred previously that used self-organizing map as the learning engine. Conclusion: Such high level reliable correct extraction of inflamed appendix encourages to use the automatic extraction software in the diagnosis procedure of suspected acute appendicitis.

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Automatic Extraction of Soft Tissue Tumor from Ultrasonography Using ART2 Based Intelligent Image Analysis

Cited by 7 publications

References 0 publications

Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

Semi-dynamic Control of FCM Initialization for Automatic Extraction of Inflamed Appendix from Ultrasonography

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