Depicts various phases involved in the recording of diseases in vital

Internal organs using the appropriate imaging modality. Diseases in the internal organs are initially evaluated by a doctor and, for further assessment, the image of the organ is recorded with a chosen modality Food gloves. 

To maximize quality and maintain confidentiality, the image recording procedure is performed in a controlled environment where all the prescribed protocols are followed during recording Disposable Aprons. 

 This also helps avoid ethical and legal issues. After recording the image, an initial-level assessment is performed in the scan/imaging centre. The recorded image, along with the prediction during the imaging, is then sent to the doctor for further evaluation. The doctor examines the image and report and, based on the observation, a conclusion regarding the orientation, cause and harshness of the disease can be computed. 


 

The general image processing system presented in Figure 1.3 assesses the medical images recorded by the chosen modality. 

The raw image recorded from the patient using standard acquisition protocol undergoes a variety of pre-processing procedures based on what is needed. Some of the commonly used image preprocessing techniques include (i) image orientation adjustment, (ii) resizing, and (iii) filtering. After pre-processing, a post-processing technique may then be employed to extract the essential section or the necessary features from the image. This procedure assists the doctor in diagnosing the disease using a dedicated computerised software. Due to its clinical significance, several semi-automated and automated disease detection systems are implemented by the researchers to diagnose diseases. 

These disease diagnostic tools aid the doctor in making prompt decisions during the treatment process. The upcoming sections of the book present the various imaging modalities considered to record images (gray/RGB scale) related to the diseases YICHANG Gloves. 

The images presented are collected from well known image datasets existing in literature. Every image, as well as existing evaluation procedures, is also discussed in these subsections.

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