Improvement of renal image recognition through resolution enhancement

Image resizing is frequently used as a preprocessing step in many computer vision tasks, especially in medical applications. While tuning of the resizing method is usually omitted in the studies, there are many problems in which the exact influence of resampling on image textures and gradients is significant. The paper presents an in-depth analysis of image reconstruction's impact on two inherent tasks in medical image analysis: segmentation and classification. The proposed study is conducted on the renal diagnosis dataset in which the kidney is segmented, and three renal tumours are classified. A novel image reconstruction method is introduced, namely Sampling Kantorovich Algorithm (SKA). It is compared to six other popular techniques widely used in image processing. Based on the qualitative and quantitative analyses, we proved that choice of image reconstruction method impacts the system's overall performance. SKA turns out to be the best performing method in the classification setup. It boosts performance to 75% of the weighted F1-score by approximately 3 percentage points (pp) compared to the best baseline solution. In kidney segmentation, the SKA improves efficiency by over 2pp compared to other resizing methods. The results presented in this paper may apply to a wide range of medical image processing problems.