## 1 Introduction  
Initial changes in superficial esophageal squamous cell carcinomas (ESSCCs) begin from the morphological alteration of intrapapillary capillary loops (IPCLs) in the esophageal mucosa. In the normal esophageal mucosa, IPCLs with regular morphologies are observed in magnifying endoscopy; however, the IPCLs are dilated with irregular morphologies and their calibers change at the esophageal cancer site. These changes in IPCLs are known to differ depending on the invasion depth of esophageal cancer [1]. In clinical practice, the prediction of the invasion depth of SESCCs is a critical factor to determine the appropriate treatment modality such as endoscopic resection or surgery. Recently, the Japan Esophageal Society (JES) proposed a simplified classification method based on the IPCL patterns for estimating the invasion depth of SESCCs [2,3]. The classification criteria for the microvessels in SESCCs are based on the IPCL morphology (Figure 1). The A type indicates normal IPCLs, while the B-types indicate abnormal IPCLs which are suggestive of esophageal cancer. The B1 type consists of abnormal microvessels with a conserved loop-like formation; the B2 type consists of stretched and markedly elongated transformations without a loop-like formation; and the B3 type consists of highly dilated, irregular vessels with a caliber that appears more than three times greater than that of the B2-type vessels. The accuracy of the JES classification ranged from 78.6%-90.5% in recent research studies [2,3].  
However, determining the morphological changes of IPCLs with the JES classification has non-negligible interobserver variability, as such decisions depend on the knowledge and experience of the endoscopist. To exclude this subjectivity, automatic software that analyzes the features of IPCLs to predict the invasive depth of SESCCs is required. Hence, if the index of the feature patterns automatically extracted by such software matches with the proven clinical results, more accurate prediction of the invasive depth of SESCCs can be provided to the endoscopists.  
In this study, a method is proposed for classifying and analyzing SESCCs using image processing and machine learning techniques on the IPCL patterns related to the invasion depth of the tumor obtained from the magnifying endoscopy images of patients with SESCCs. To achieve the proposed goal, several machine learning algorithms, such as the adaptive resonance theory 2 (ART2) algorithm, were applied to isolate the vessel area from the image and the support vector machine (SVM) to classify the IPCL patterns according to the JES classification in the magnifying endoscopy images. An algorithm for blood vessel thickness measurement is also proposed. The morphological features of IPCLs extracted from the magnifying endoscopy images of SESCCs are first classified into the B1 and non-B1 types using the learning patterns of the SVM algorithm with the non-B1 type subsequently classified into the B2 and B3 types based on the thicknesses of the IPCLs.  
The overall structure of this paper is organized as follows. Section 2 describes related studies such as the SVM and conventional vessel thickness measurement algorithms employed in this study. Section 3 describes the proposed method. Section 4 describes the experiments and analysis results of the magnifying endoscopy images of SESCCs. Finally, Section 5 presents the discussion of this study and some future research directions.