Morphological and fractal features of cancer cells anchored on composite layers based on magnesium-doped hydroxyapatite loaded in chitosan matrix

In the present study, we report the development and characterization of composite layers (by spin coating) based on magnesium-doped hydroxyapatite in a chitosan matrix, containing human osteosarcoma MG63 cells anchored. Studies regarding the biocompatibility of the composite layers were performed with the aid of a MTT (3-4,5-Dimethylthiazol 2,5-diphenyltetrazolium bromide) assay. The data determined that the composite layers did not inhibit the growth and adhesion of MG63 cells to their surfaces exhibiting good biocompatibility properties. Furthermore, the attachment and development of MG63 cells on the surface of MgHApCh composite layers were investigated using atomic force microscopy (AFM). AFM topographical maps emphasized that the HApCh and 8MgHApCh composite layers surface promoted the attachment and proliferation of MG63 cells on their surface. Meanwhile, in the case of 30MgHApCh layers incubated for 48 h, a slight modification of the morphological features of the MG63 cells. In addition, the effects of the composite layers against Candida albicans ATCC 10231 were also evaluated. The data results from the in vitro antifungal assay depicted that the composite layers successfully inhibited the growth of the fungal cells onto their surface. Morphological and fractal analyses unveil cancer cell surfaces on Mg-containing composite layers with intricate 3D patterns, driven by high-frequency components. Their remarkable complexity and roughness arises from a strong multifractal nature, supporting more effective vertical growth compared to Si and HApCh surfaces. The cell viability reduced of uncoated Si surface is highlighted by its less intense 3D pattern growth. Our results show that the uncoated Si surface promotes lower viability of MG63 cancer cells, with less rough and complex 3D spatial patterns.

Percolation Images: Fractal Geometry Features for Brain Tumor Classification

Brain tumor detection is crucial for clinical diagnosis and efficient therapy. In this work, we propose a hybrid approach for brain tumor classification based on both fractal geometry features and deep learning. In our proposed framework, we adopt the concept of fractal geometry to generate a "percolation" image with the aim of highlighting important spatial properties in brain images. Then both the original and the percolation images are provided as input to a convolutional neural network to detect the tumor. Extensive experiments, carried out on a well-known benchmark dataset, indicate that using percolation images can help the system perform better.

Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions

Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database.