Abstract:
An important feature of image analysis is texture, seen in all images, from
aerial and satellite images to microscopic images in biomedical research. A
chest X-ray is the most common and effective method for diagnosing severe
lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the
largest X-ray object. The correct separation of the shapes and sizes of the
contours of the lungs is an important reason for diagnosis, because of which
an intelligent information environment can be created. Despite the use of
X-rays, to identify the diagnosis, there is a chance that the disease will not be
detected. In this sense, there is a risk of development, which may be fatal.
The article deals with the problems of pneumonia clustering using the
autocorrelation function to obtain the most accurate result. This provides a
reliable tool for diagnosing lung radiographs. Image pre-processing and data
shaping play an important role in revealing a well-functioning basis of the
nervous system. Therefore, images from two classes were selected for the
task: healthy and with pneumonia. This paper demonstrates the applicability
of the autocorrelation function for highlighting interest in lung radiographs
based on the fineness of textural features and k-means extraction.
