Methods and systems for performing segmentation and registration of images using neutrosophic similarity scores
An example method for segmenting an object contained in an image includes receiving an image including a plurality of pixels, transforming a plurality of characteristics of a pixel into respective neutrosophic set domains, calculating a neutrosophic similarity score for the pixel based on the respective neutrosophic set domains for the characteristics of the pixel, segmenting an object from background of the image using a region growing algorithm based on the neutrosophic similarity score for the pixel, and receiving a margin adjustment related to the object segmented from the background of the image.
1. A method for segmenting an object contained in an image, comprising:
receiving, using at least one processor, an image including a plurality of pixels;
transforming, using the at least one processor, a plurality of characteristics of a pixel into respective neutrosophic set domains;
calculating, using the at least one processor, a neutrosophic similarity score for the pixel using a respective neutrosophic set domain for each of the characteristics of the pixel;
segmenting, using the at least one processor, an object from background of the image using a region growing algorithm based on the neutrosophic similarity score for the pixel; and
receiving, using the at least one processor, a margin adjustment related to the object segmented from the background of the image, wherein the respective neutrosophic set domains comprise a neutrosophic set domain for intensity values and a neutrosophic set domain for homogeneity values, and wherein each of the respective neutrosophic set domains includes a true value, an indeterminate value, and a false value.
2. The method of claim 1 , further comprising:
receiving, using the at least one processor, an annotation related to the object segmented from the background of the image; and
storing, using the at least one processor, the annotation related to the object segmented from the background of the image.
3. The method of claim 1 , wherein the pixel is merged into a region containing the object under the condition that the neutrosophic similarity score for the pixel is less than a threshold value.
4. The method of claim 1 , wherein the pixel is merged into a region containing the background under the condition that the neutrosophic similarity score for the pixel is greater than a threshold value.
5. The method of claim 1 , wherein the plurality of characteristics comprise an intensity, a textural value and a homogeneity of the pixel.
6. The method of claim 5 , wherein calculating the neutrosophic similarity score for the pixel based on the respective neutrosophic set domains for the characteristics of the pixel further comprises:
calculating respective neutrosophic similarity scores for each of the respective neutrosophic set domains; and
calculating a mean of the respective neutrosophic similarity scores for each of the respective neutrosophic set domains.
7. The method of claim 5 , wherein the intensity of the pixel is transformed into the neutrosophic set domain for intensity values based on the intensity value.
8. The method of claim 5 , wherein the homogeneity of the pixel is transformed into the neutrosophic set domain for homogeneity values based on the homogeneity value.
9. The method of claim 8 , further comprising filtering the image to obtain the homogeneity of the pixel.
10. The method of claim 1 , wherein the image provides a 2D or 3D visualization of the object.
11. The method of claim 1 , wherein the object includes a lesion region of interest.
12. The method of claim 1 , wherein the at least one processor is part of a cloud computing environment.