Moh'd Belal Al-Zoubi

Segmentation of lumbar vertebrae from clinical CT using active shape models and GVF-snake

Lumbar area of the vertebral column bears the most load of the human body and thus it is responsible for the major portion of lower back pain from which 80% to 90% of people suffer from during their lifetime. Vertebra related diseases are mainly fracture and are usually diagnosed from X-ray radiographs or CT scans depending on the severity of the problem. In this paper, we propose a fully automated lumbar vertebra segmentation that accurately and robustly produces a smooth contour around each of the vertebrae. This segmentation is very useful in any subsequent CAD system for diagnosis and quantification of vertebrae fractures. It also serves the radiologist during the clinical routine. Our method shows an excellent level of vertebra boundary smootheness that was visually approved by our collaborating radiologist for each vertebra and each case from our fifty cases dataset that includes both normal and abnormal cases.

Image contrast enhancement using geometric mean filter

Histogram equalization HE is one of the most popular methods for image contrast enhancement. However, the intensity of the input image plays an important role on its performance. In particular, HE fails to enhance images with a dominant color. Therefore, several techniques were proposed to tackle this problem. Some are built for brightness preservation, and others aim to maximize the preservation of structural information. In this paper, we propose an efficient HE enhancement technique that is not only addresses brightness preservation but also both edge and structural information preservation. The proposed technique investigates the geometric mean filter for smoothing the peaks in the histogram before applying the HE. To support our claims, a set of experiments were conducted. Remarkably, through qualitative and quantitative evaluations, results demonstrate that the performance of the proposed method, when compared with a set of other state-of-the-art methods including HE, CLAHE, Log-Power, BPDFHE and DWT–SVD, shows a significant improvement especially in terms of structural and edge preservation.

A new algorithm for automatic extraction of GIS layers

GIS layers have became an essential part of any GIS model today, and therefore, there is a need for developing automatic approaches to extract GIS layers from digital map images (scanned maps). This is due to the increasing number of digital maps and the advances in the functionality of modern Geographic Information Systems (GIS). In this paper, we propose a new approach that can be used to automatically extract GIS layers based on the HSI color model. The results are acceptable.

Histopathological prostate tissue glands segmentation for automated diagnosis

In this work, we propose a methodology for segmenting glands automatically in digitized images of histopathological prostate tissue for grade classification. Gleason grading describes the abnormality of cancer cells and their degree of aggressiveness by using numerical scale from grade 1 that represents benign tissues through grade 5 for tissues characterized as advanced stage cancer. The special characteristics of glands in prostate tissue for each grade play a significant role in discriminating Gleason grades. Therefore, lumen objects and tissue glands were segmented as the major regions of interest for tissue grading. Lumen objects were segmented by an empirical thresholding technique. Since we are mainly concerned with the inner regions of the glands consisting of the lumen, cytoplasm and the inner boundary of the cell nuclei, a k-means clustering approach was employed to the a* color channel of the L*a*b* color model for each of the tissue images. This was followed by statistical and morphological features extraction for the segmented lumen objects and glands. Finally, a naive Bayes classifier was used to classify tissue images to the correct grade. The efficiency of the automated segmentation method was evaluated, and classification results achieved accuracy, sensitivity, and specificity of 91.66%, 96.66%, and 95.00%, respectively. These results indicate that our automated methodology could serve as an adjunct to histopathologists and would have a positive impact when integrated with conventional histopathological diagnosis procedures.