Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec 26;23(1):235.
doi: 10.3390/s23010235.

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

Khalaf Alshamrani  1 , Hassan A Alshamrani  1 , Fawaz F Alqahtani  1 , Bander S Almutairi  2
Affiliations

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

Khalaf Alshamrani et al. Sensors (Basel). .

Abstract

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients' lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image's background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.

Keywords: CAD system; CLAHE; HIW; cancer classification; malignant calcifications; mammography histogram.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Structure of the convolutional neural network used in this work. Each of the layers is shown accompanied by the dimensionality of the input and output data.
Figure 2
Figure 2
Diagram of the procedure for obtaining the cuts.
Figure 3
Figure 3
Images obtained during the training of the training set.
Figure 4
Figure 4
Histograms of mammograms in CC (a) and MLO (b) views.
Figure 5
Figure 5
Thresholds on the histogram of a mammographic image in CC view and tissues that make up the image.
Figure 6
Figure 6
Distributions available in the CLAHE function and their histograms. From left to right uniform, Rayleigh, and exponential.
Figure 7
Figure 7
Ranges available in the CLAHE function and their histograms.
Figure 8
Figure 8
Results obtained during the test-0 to test-6.
See this image and copyright information in PMC

References

    1. Sopik V., Narod S.A. The relationship between tumour size, nodal status and distant metastases: On the origins of breast cancer. Breast Cancer Res. Treat. 2018;170:647–656. doi: 10.1007/s10549-018-4796-9. - DOI - PMC - PubMed
    1. Zhou Y., Mao F., Wang C., Lin Y., Xiao Y., Pan B., Zhou X., Yao R., Sun Q. Breast cancer detected only by positron emission tomography with extensive osteolytic bone metastases mimicked Multiple Myeloma: A case report. Eur. J. Mol. Clin. Med. 2017;1:25. doi: 10.1016/j.nhccr.2017.06.193. - DOI
    1. Hamad A.A., Thivagar M.L., Alazzam M.B., Alassery F., Khalil M.M., Ramesh V., Meraf Z., Kumar V. Dynamic Systems Enhanced by Electronic Circuits on 7D. Adv. Mater. Sci. Eng. 2021;2021:1–11. doi: 10.1155/2021/8148772. - DOI
    1. Chiang D., Lee M., Germaine P., Liao L. Amyloidosis of the Breast with Multicentric DCIS and Pleomorphic Invasive Lobular Carcinoma in a Patient with Underlying Extranodal Castleman’s Disease. Case Rep. Radiol. 2013;2013:1–3. doi: 10.1155/2013/190856. - DOI - PMC - PubMed
    1. Al-Obeidi A.S., Al-Azzawi S.F., Hamad A.A., Thivagar M.L., Meraf Z., Ahmad S. A Novel of New 7D Hyperchaotic System with Self-Excited Attractors and Its Hybrid Synchronization. Comput. Intell. Neurosci. 2021;2021:1–11. doi: 10.1155/2021/3081345. - DOI - PMC - PubMed