Systems and articles of manufacture for image segmentation are provided herein, and include creating an anatomical model from training data comprising one or more imaging modalities, generating one or more simulated images in a target modality based on the anatomical model and one or more principles of physics pertaining to image contrast generation, and comparing the one or more simulated images to an unlabeled input image of a given imaging modality to determine a simulated image of the one or more simulated images to represent the unlabeled input image.

Comments on Two Papers in Pattern Recognition

Image segmentation techniques

The use of variance and entropic thresholding methods for image segmentation

Due to the imperfection of image acquisition systems, the acquired images are subjected to various defects that will affect the subsequent image processing. Although these defects can sometimes be corrected by adjusting the acquisition hardware, such as increasing the number of images captured for the same scene and adopting higher quality instruments, the hardware-based solutions are time-consuming and costly. Therefore it is preferred to correct the images using computer programs, which are fast and not very costly. For example, smooth filters, including linear and median filters, can be applied to remove noise and contrast can be enhanced by scaling or equalizing the image histograms in low contrast images. These image defect corrections are generally called image preprocessing.

Comments on Two Papers in Pattern Recognition

Citations

Image segmentation techniques

The use of variance and entropic thresholding methods for image segmentation

Image Segmentation Techniques

2 – Image Segmentation Techniques

References

A threshold selection method from gray level histograms

Picture thresholding using an iterative selection method.

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