An important preliminary step in the diagnosis of leukemia is the visual examination of the patient's peripheral blood smear under the microscope. Morphological changes in the white blood cells can be an indicator of the nature and severity of the disease. Manual techniques are labor intensive, slow, error prone and costly. A computerized system can be used as a supportive tool for the specialist in order to enhance and accelerate the morphological analysis process. This research present a new method that integrates color features with the morphological reconstruction to localize and isolate lymphoblast cells from a microscope image that contains many cells. The localization and segmentation are conducted using a proposed method that consists of an integration of several digital image processing techniques. 180 microscopic blood images were tested, and the proposed framework managed to obtain 100% accuracy for the localization of the lymphoblast cells and separate it from the image scene. The results obtained indicate that the proposed method can be safely used for the purpose of lymphoblast cells localization and segmentation and subsequently, aiding the diagnosis of leukemia.
The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a novel feature: the number of colors present in lesions, which can indicate the severity of skin diseases and help distinguish melanomas from benign lesions. We propose a color histogram analysis, a traditional image processing technique, to analyze the pixels of skin lesions from three publicly available datasets: PH2, ISIC2016, and Med-Node, which include dermoscopic and non-dermoscopic images. While the PH2 dataset contains ground truth about skin lesion colors, the ISIC2016 and Med-Node datasets lack such annotations; our algorithm establishes this ground truth using the color histogram analysis based on the PH2 dataset. We then design and train a 19-layer Convolutional Neural Network (CNN) with different skip connections of residual blocks to classify lesions into three categories based on the number of colors present. The DeepDream algorithm is utilized to visualize the learned features of different layers, and multiple configurations of the proposed CNN are tested, achieving the highest weighted F1-score of 75.00 % on the test set. LIME is subsequently applied to identify the most important features influencing the model's decision-making. The findings demonstrate that the number of colors in lesions is a significant feature for describing skin conditions. The proposed CNN, particularly with three skip connections, shows strong potential for clinical application in diagnosing melanoma, supporting its use alongside traditional diagnostic methods.
Recently, determination of skeletal age, defined as the assessment of bone age, has rapidly become an important task between forensic experts and radiologists. The Greulich-Pyle (GP) atlas is one of the most frequently used methods for the assessment of skeletal age around the world. After presentation of the GP approach for the estimation of the bone age, much research has been conducted to examine the usability of this method in various geographic or ethnic categories. This study investigates on a small-scale and compares the reliability of the GP atlas for assessment of the bone age for four ethnic groups - Asian, African/American, Caucasian and Hispanic - for a different range of ages.
Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.
The incidence of oral cancer is high for those of Indian ethnic origin in Malaysia. Various clinical and pathological data are usually used in oral cancer prognosis. However, due to time, cost and tissue limitations, the number of prognosis variables need to be reduced. In this research, we demonstrated the use of feature selection methods to select a subset of variables that is highly predictive of oral cancer prognosis. The objective is to reduce the number of input variables, thus to identify the key clinicopathologic (input) variables of oral cancer prognosis based on the data collected in the Malaysian scenario. Two feature selection methods, genetic algorithm (wrapper approach) and Pearson's correlation coefficient (filter approach) were implemented and compared with single-input models and a full-input model. The results showed that the reduced models with feature selection method are able to produce more accurate prognosis results than the full-input model and single-input model, with the Pearson's correlation coefficient achieving the most promising results.
Blastocystis sp. is an enteric protistan parasite that affects individuals worldwide with gastrointestinal symptoms such as abdominal discomfort, diarrhea, and flatulence. However, its pathogenicity is controversial due to its presence among asymptomatic individuals. Blastocystis sp. subtype 3 (ST3) is the most prevalent subtype among humans that have been associated with irritable bowel syndrome (IBS), Crohn's disease, ulcerative colitis, and colorectal cancer. Axenization of the parasite has been shown to impede its growth thus revealing the importance of accompanying bacteria in ensuring Blastocystis sp. survival. This study aims to identify the influence of accompanying bacteria on the growth of Blastocystis sp. ST3. Blastocystis sp. cultures were treated with Meropenem, Vancomycin, and Amoxicillin-Clavulanic acid (Augmentin). Bacteria-containing supernatant of antibiotic-treated and control cultures were isolated and identified through 16 s rRNA sequencing. Morphological changes of antibiotic-treated Blastocystis sp. ST3 were also observed. The cultures treated with meropenem and augmentin exhibited opposing effects with reduced growth of isolates from symptomatic patients and a significant increase in asymptomatic isolates. Whereas, vancomycin-treated cultures had no difference in the growth of Blastocystis sp. ST3 isolates from symptomatic and asymptomatic patients. Isolates from symptomatic and asymtomatic patients had 6 and 2 distinct bacterial species identified with Proteus mirabilis as the common bacteria among both types of isolates. Morphologically, Blastocystis sp. ST3 cultures exposed to meropenem and augmentin demonstrated an increase in pre-cystic forms. These findings demonstrate the effects of accompanying bacteria on the growth of Blastocystis sp. ST3 that could translate into clinical manifestations observed among Blastocystis sp.-infected patients.