We propose a new method for automatically detecting early esophageal cancer from an endoscopic image. We decompose the original image into four components, namely, the RGB and luminance components, and apply the discrete wavelet transform (DWT) to these components twice. The fractal dimensions are computed at each small block using the box-counting method, and the abnormal regions are detected based on the fractal dimensions. In addition, to process the endoscopic image quickly, we clip the portion that does not contain the esophageal cancer from the original endoscopic image and resize the remaining image to 1024 × 1024 pixels by mirroring. Finally, we show the abnormal region by using the product of the computed fractal dimensions from the four components. Our method is not intended for providing accurate diagnosis of the detected abnormal regions as esophageal cancer, but is intended to provide additional information to help doctors in their diagnosis. Therefore, our method only needs to detect all of the regions suspected of being cancer even if the detect results are false positives. We describe the procedure used in our method in detail and present experimental results demonstrating that our method is able to detect abnormal regions suspected of being early esophageal cancer using practical endoscopic images.

We propose several new no-reference/blind image quality indices for blur assessment based on the discrete wavelet transform (DWT) and demonstrate that a given image can be classified based on blur by using these indices. Our approach relies on the sharpness, granularity, and L1-norm estimation of the given image in the DWT domains with a relatively long support width. Unlike conventional methods, the reference image is produced from the given image without computing special statistics or using unsupervised methods. Instead, we produce the reference image from the given image by using certain sharpening and denoising methods, and by adopting ideas from reference image quality measures such as the PSNR and SSIM in the DWT domain. We describe the detailed procedure of our method and show some experimental results that demonstrate the high performance.