Measurement of smaller colon polyp in CT colonography images using morphological image processing

A Systematic Approach of Data Collection and Analysis in Medical Imaging Research

Background:

Obtaining the right image dataset for the medical image research systematically is a tedious task. Anatomy segmentation is the key step before extracting the radiomic features from these images.

Objective:

The purpose of the study was to segment the 3D colon from CT images and to measure the smaller polyps using image processing techniques. This require huge number of samples for statistical analysis. Our objective was to systematically classify and arrange the dataset based on the parameters of interest so that the empirical testing becomes easier in medical image research.

Materials and Methods:

This paper discusses a systematic approach of data collection and analysis before using it for empirical testing. In this research the image were considered from National Cancer Institute (NCI). TCIA from NCI has a vast collection of diagnostic quality images for the research community. These datasets were classified before empirical testing of the research objectives. The images in the TCIA collection were acquired as per the standard protocol defined by the American College of Radiology. Patients in the age group of 50-80 years were involved in various clinical trials (multicenter). The dataset collection has more than 10 billion of DICOM images of various anatomies. In this study, the number of samples considered for empirical testing was 300 (n) acquired from both supine and prone positions. The datasets were classified based on the parameters of interest. The classified dataset makes the dataset selection easier during empirical testing. The images were validated for the data completeness as per the DICOM standard of the 2020b version. A case study of CT Colonography dataset is discussed.

Conclusion:

With this systematic approach of data collection and classification, analysis will be become more easier during empirical testing.

Data Integrity of Radiology Images Over an Insecure Network Using AES Technique

BACKGROUND

While transmitting the medical images in radiology information systems the adversary effect can break the CIA (Confidentiality, Integrity, and Availability) triads of information security. The objective of the study was to transmit the complete set of image objects in a dataset without data integrity violation.

METHODS

In this paper a hybrid cryptographic technique which combines the prime details from the patient dataset (stack of axial 2D images) and the Advanced Encryption Standard (AES) method has been proposed. The steps include a) Creating an artificial X-ray image (DRR) from the 3D volume, b) dividing the DRR image in x and y directions equally into four regions, c) applying the zig-zag pattern to each quadrant, and d) encryption of each quadrant with block cipher mode using the AES algorithm. After dataset transmission the DRR image was regenerated at the receiver and compared each of the deciphered blocks (transmitted ones) using the histogram technique.

RESULTS

The technique was tested on CT and MRI scans of sixty datasets. The image injection techniques, such as adding and deleting an image from the dataset and modifying the image pixels, were tested. The results were validated statistically using mean square error and histogram matching techniques.

CONCLUSION

The combination of the DRR and the AES technique has ensured the secured transmission of the entire dataset and not an individual image.