System and method associated with predicting segmentation quality of objects in analysis of copious image data

1. A system for predicting segmentation quality of segmented objects implemented in the analysis of copious image data, the system comprising:

a quality assessment engine including a computing device configured to train the system during a training phase by performing operations comprising:

receiving a collection of image data related to a particular type of data;

partitioning the image data into segmented data portions based on an object associated with the collection of image data;

determining regions of interest associated with the segmented data portions;

determining quality of segmentation of the segmented data portions for respective classification of the regions of interest, the quality of segmentation being determined based on patch-level intensity and texture features associated with the image data;

assigning a classification label to the regions of interest, the classification label describing a quality of segmentation of the objects associated with the segmented data portions;

partitioning regions of interest into sub-regions;

computing a set of intensity and texture features for each of the sub-regions of the segmented data portions; and

generating a training dataset based on the computed intensity and texture features for the sub-regions in order to train a classification model based on a predetermined threshold value; and

the computing device in the quality assessment engine being configured to predict segmentation quality during a prediction phase by performing operations comprising:

receiving test images to iteratively classify segmented data portions based on an object associated with the test images, using the trained classification model; and

predicting the segmentation quality of segmented objects in the test images based on the trained classification model.

2. The system as recited in claim 1 , wherein the classification label includes at least one of: bad segmentation, under-segmented, over-segmented and good segmentation of the objects associated with the segmented data portions.

3. The system as recited in claim 1 , wherein regions of interest are segmented into sub-regions of equal-sized areas.

4. The system as recited in claim 1 , which further comprises adjusting the predetermined threshold value to achieve more refined segmentation quality.

5. The system as recited in claim 4 , which further comprises the predetermined threshold value comprising a gain value being adjusted in the range of a 0.0 and 2.0, in order to adjust respective intensity differences.

6. The system as recited in claim 1 , which further comprises the objects being one of: a biological object, a cellular object, an artificial structure associated with a map, a natural structure associated with a map, a miscellaneous object located on earth, and a miscellaneous object surrounding the atmosphere above earth.

7. A method for predicting segmentation quality of segmented objects implemented in the analysis of copious image data, the method comprising:

a quality assessment engine including a computing device configured to train the system during a training phase by performing operations comprising:

receiving a collection of image data related to a particular type of data;

partitioning the image data into segmented data portions based on an object associated with the collection of image data;

determining regions of interest associated with the segmented data portions;

determining quality of segmentation of the segmented data portions for respective classification of the regions of interest, the quality of segmentation being determined based on patch-level intensity and texture features associated with the image data;

assigning a classification label to the regions of interest, the classification label describing a quality of segmentation of the objects associated with the segmented data portions;

partitioning regions of interest into sub-regions;

computing a set of intensity and texture features for each of the sub-regions of the segmented data portions; and

generating a training dataset based on the computed intensity and texture features for the sub-regions in order to train a classification model based on a predetermined threshold value; and

the computing device in the quality assessment engine being configured to predict segmentation quality during a prediction phase by performing operations comprising:

receiving test images to iteratively classify segmented data portions based on an object associated with the test images, using the trained classification model; and

predicting the segmentation quality of segmented objects in the test images based on the trained classification model.

8. The method as recited in claim 7 , wherein the classification label includes at least one of: bad segmentation, under-segmented, over-segmented and good segmentation of the objects associated with the segmented data portions.

9. The method as recited in claim 7 , wherein regions of interest are segmented into sub-regions of equal-sized areas.

10. The method as recited in claim 7 , which further comprises adjusting the predetermined threshold value to achieve more refined segmentation quality.

11. The method as recited in claim 10 , which further comprises the predetermined threshold value being a gain value being adjusted in the range of a 0.0 and 2.0, in order to adjust respective intensity differences.

12. The method as recited in claim 7 , which further comprises the objects being one of: a biological object, a cellular object, an artificial structure associated with a map, a natural structure associated with a map, a miscellaneous object located on earth, and a miscellaneous object surrounding the atmosphere above earth.

13. A computer-readable device storing instructions for predicting segmentation quality of segmented objects implemented in an analysis of copious image data that, when executed by a processing device, are configured to train a system for predicting segmentation quality of segmented objects during a training phase by performing operations comprising:

receiving a collection of image data related to a particular type of data;

partitioning the image data into segmented data portions based on an object associated with the collection of image data;

determining regions of interest associated with the segmented data portions;

determining quality of segmentation of the segmented data portions for respective classification of the regions of interest, the quality of segmentation being determined based on patch-level intensity and texture features associated with the image data;

assigning a classification label to the regions of interest;

partitioning regions of interest into sub-regions;

computing a set of intensity and texture features for each of the sub-regions of the segmented data portions; and

generating a training dataset based on the computed intensity and texture features for the sub-regions in order to train a classification model based on a predetermined threshold value; and

the instructions stored by the computer-readable device further configured to predict segmentation quality during a prediction phase by performing operations comprising:

receiving test images to iteratively classify segmented data portions based on an object associated with the test images, using the trained classification model; and

predicting the segmentation quality of segmented objects in the test images based on the trained classification model.

14. The computer-readable device as recited in claim 13 , wherein the objects are one of: a biological object, a cellular object, an artificial structure associated with a map, a natural structure associated with a map, a miscellaneous object located on earth, and a miscellaneous object surrounding the atmosphere above earth.