An approach based on Fourier descriptors and decision trees to perform presumptive diagnosis of esophagitis for educational purposes

Serpa-Andrade, Luis; Robles-Bykbaev, Vladimir; Gonzalez-Delgado, Luis; Moreno, J

doi:10.1109/ropec.2015.7395123

Cited by 8 publications

(8 citation statements)

References 5 publications

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“…Serpa-Andrade et al [44] proposed a method in which the esophagitis (a condition of chronic BE stage) was described using Fourier Transform on the Z-line signature (esophageal irregularities) for classification purposes. The proposed descriptors were based on statical features and textural information.…”

Section: Comparison Among Classifiers For Barrett's Esophagus Recognimentioning

confidence: 99%

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A survey on Barrett's esophagus analysis using machine learning

Souza

Palm

Mendel

et al. 2018

Computers in Biology and Medicine

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This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.

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Section: Comparison Among Classifiers For Barrett's Esophagus Recognimentioning

confidence: 99%

“…Serpa-Andrade et al [44] k-NN and Random Forests 10 endoscopic images of healthy tissue and 16 images of ill tissue cross-validation proposed a method in which the esophagitis (a condition of cronic BE stage) was described using Fourier Transform on the Z-line signature for classification purposes.…”

Section: -Fold Crossvalidationmentioning

confidence: 99%

A survey on Barrett's esophagus analysis using machine learning

Souza

Palm

Mendel

et al. 2018

Computers in Biology and Medicine

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“…Different values of the parameters defined in [30] result in the best values of 86.1, 56.8, and 95.3 for accuracy, recall, and specificity criteria, respectively. No evaluation is done for the Z‐line segmentation method in [31], and the results are only reported for the classification. For BE and Polyp segmentation in [32], the best result is achieved in phase II.…”

Section: Discussionmentioning

confidence: 99%

“…However, BE and EAC regions are more recognizable than the regions that suffer from early‐stage Barrett (our case) due to their tissue deformation and/or colour changes. Therefore, the methods presented in [28–31, 33–36] could not help annotate the Z‐line in our dataset. Along with this reason, the deep learning‐based methods provided in [13, 18, 32] cannot be suitable due to the difference in the quality of the images.…”

Section: Discussionmentioning

confidence: 99%

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Automatic esophagus Z‐line delineation in endoscopic images using a new boundary linking method

Aghanouri

Serej

Rabbani

et al. 2022

IET Image Processing

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Due to the American cancer society, many people with esophageal adenocarcinoma are not survived. The treatment rate can be significant in the early detection of Barrett's esophagus (BE) as a premalignant stage for adenocarcinoma. An important landmark to detect BE is the Z-line. BE segmentation is already highly dependent upon the operator's knowledge and skill. The main aim of this study is automatic Z-line extraction using endoscopic images leading to segmentation of the early BE stage. To this end, a computer-aided detection method exploiting k-means clustering, image segmentation using the edge detector, and a novel boundary linking algorithm is proposed. For the evaluation, the gold standard is considered the average contours of Z-lines extracted by the three experts. The proposed method annotated the Z-line with the accuracy and precision of 0.92 and 0.87, respectively, and the value of the average boundary distance is 5.9 pixels. To the results and visual inspection, the presented method can be used for efficient and robust extraction of the Zline at the early BE stage. Furthermore, it can be used in other medical imaging applications with complex boundaries and low contrast in the images, limiting the common automatic boundary detection methods.

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