RESUMO
Skin burns in color images must be accurately detected and classified according to burn degree in order to assist clinicians during diagnosis and early treatment. Especially in emergency cases in which clinical experience might not be available to conduct a thorough examination with high accuracy, an automated assessment may benefit patient outcomes. In this work, detection and classification of burnt areas are performed by using the sparse representation of feature vectors by over-redundant dictionaries. Feature vectors are extracted from image patches so that each patch is assigned to a class representing a burn degree. Using color and texture information as features, detection and classification achieved 95.65% sensitivity and 94.02% precision. Experiments used two methods to build dictionaries for burn severity classes to apply to observed skin regions: (1) direct collection of feature vectors from patches in various images and locations and (2) collection of feature vectors followed by dictionary learning accompanied by K-singular value decomposition.
Assuntos
Algoritmos , HumanosRESUMO
Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data.