Texture features in medical image analysis: a survey
원문 초록
The texture is defined as spatial structure of the intensities of the pixels in an image that is repeated periodically in the whole image or regions, and makes the concept of the image. Texture, color and shape are three main components which are used by human visual system to recognize image contents. In this paper, first of all, efficient and updated texture analysis operators are survived with details. Next, some state-of-the-art methods are survived that use texture analysis in medical applications and disease diagnosis. Finally, different approaches are compared in terms of accuracy, dataset, application, etc. Results demonstrate that texture features separately or in joint of different feature sets such as deep, color or shape features provide high accuracy in medical image classification.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Deep Modeling and Optimization of Medical Image Classification
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models
MUPAX: Multidimensional Problem Agnostic eXplainable AI
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise
Robust Training with Data Augmentation for Medical Imaging Classification
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.