Block-Based Texture Features for Chromoendoscopy Classification

Globally, there has been an upsurge in concern about stomach cancer in recent years. One of the key factors contributing to gastrointestinal (GI) tract abnormalities, such as ulcers and inflammation, is an improper diet. Additionally, these anomalies might aid in the growth of stomach cancer. Compared to biopsy, endoscopy is a less invasive way to screen gastric cancer and other GI tract abnormalities. In chromoendoscopy, one of the advancements to endoscopy, abnormal regions are made more visible visually by spraying colors over the mucosal surface. However, identifying abnormal regions in the frames taken during a chromoendoscopic session is still challenging. Basic visual cues like textures help to identify objects. They are helpful for inspecting abnormal spots in endoscopic frames as well. Several methods for representing texture and classifying endoscopic images have been developed. In this study, we proposed block-based texture features, especially Block Difference of Inverse Probabilities (BIDP) and Block Variation of Local Correlation Coefficients (BVLC), to represent the chromoendoscopic texture. The extracted features are then used to classify the chromoendoscopic image into normal and abnormal. On the dataset obtained using an Olympus CV-180 endoscope at the Portuguese Institute of Oncology (IPO) Hospital in Porto, Portugal, the proposed scheme has a classification accuracy of 88.9% and an area under the curve (AUC) value of 0.95, according to experimental results. It is proved that combining the BDIP and BLVC texture descriptors exhibits an excellent discrimination performance on the chromoendoscopic images. Though the proposed method is limited to chromoendoscopic images, it has the potential to be generalized by using images obtained from various imaging modalities under a variety of unhealthy conditions.