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Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.

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Thermoplastic polyurethane (TPU) is a polymer used in a variety of fields, including medical applications. Here, we aimed to verify if the brush and bar coater deposition techniques did not alter TPU properties. The topography of the TPU-modified surfaces was studied via AFM demonstrating no significant differences between brush and bar coater-modified surfaces, compared to the un-modified TPU (TPU Film). The effect of the surfaces on planktonic bacteria, evaluated by MTT assay, demonstrated their anti-adhesive effect on E. coli, while the bar coater significantly reduced staphylococcal planktonic adhesion and both bacterial biofilms compared to other samples. Interestingly, Pearson's R coefficient analysis showed that Ra roughness and Haralick's correlation feature were trend predictors for planktonic bacterial cells adhesion. The surface adhesion property was evaluated against NIH-3T3 murine fibroblasts by MTT and against human fibrinogen and human platelet-rich plasma by ELISA and LDH assay, respectively. An indirect cytotoxicity experiment against NIH-3T3 confirmed the biocompatibility of the TPUs. Overall, the results indicated that the deposition techniques did not alter the antibacterial and anti-adhesive surface properties of modified TPU compared to un-modified TPU, nor its bio- and hemocompatibility, confirming the suitability of TPU brush and bar coater films in the biomedical and pharmaceutical fields.

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In breast cancer (BC), pathologists visually score ER, PR, HER2, and Ki67 biomarkers to assess tumor properties and predict patient outcomes. This does not systematically account for intratumoral heterogeneity (ITH) which has been reported to provide prognostic value. This study utilized digital image analysis (DIA) and computational pathology methods to investigate the prognostic value of ITH indicators in ER-positive (ER+) HER2-negative (HER2-) BC patients. Whole slide images (WSIs) of surgically excised specimens stained for ER, PR, Ki67, and HER2 from 254 patients were used. DIA with tumor tissue segmentation and detection of biomarker-positive cells was performed. The DIA-generated data were subsampled by a hexagonal grid to compute Haralick's texture indicators for ER, PR, and Ki67. Cox regression analyses were performed to assess the prognostic significance of the immunohistochemistry (IHC) and ITH indicators in the context of clinicopathologic variables. In multivariable analysis, the ITH of Ki67-positive cells, measured by Haralick's texture entropy, emerged as an independent predictor of worse BC-specific survival (BCSS) (hazard ratio (HR) = 2.64, p-value = 0.0049), along with lymph node involvement (HR = 2.26, p-value = 0.0195). Remarkably, the entropy representing the spatial disarrangement of tumor proliferation outperformed the proliferation rate per se established either by pathology reports or DIA. We conclude that the Ki67 entropy indicator enables a more comprehensive risk assessment with regard to BCSS, especially in cases with borderline Ki67 proliferation rates. The study further demonstrates the benefits of high-capacity DIA-generated data for quantifying the essentially subvisual ITH properties.

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To evaluate the image quality (IQ) of advanced modeled iterative reconstruction (ADMIRE; Siemens Healthcare, Forchheim, Germany) applying image texture and image visual impression as a supplement to physical parameters such as noise level and spatial resolution. An ACR-phantom with four modules was examined at different radiation dose levels. To characterise the image texture, two Haralick texture parameters, contrast and entropy, were assessed at different dose levels and reconstruction algorithms. The visual impression of images and the low-contrast detectability were evaluated by the structural similarity index (SSIM). The spatial resolution was determined by the modulation transfer functions and the line spread function. The Haralick texture parameters, contrast and entropy, decreased with increasing ADMIRE levels. ADMIRE III, IV and V offered a comparable contrast and entropy to those calculated by filtered back projection (FBP) with a radiation dose reduction up to 50%. SSIM (low-contrast detectability) improved with increasing ADMIRE levels. SSIM calculated by ADMIRE IV and V revealed comparable IQ to FBP with a decreased CTDIvolup to 50%. Spatial resolution was retained up to 90% dose reduction. Compared to FBP at the same dose level, the image noise decreased up to 61% with higher ADMIRE levels (σFBP= 17.3 HU andσADMIREV= 10.6 HU at 6.65 mGy). Taking texture analysis and visual perception into account, a more realistic assessment of the dose reduction potential of ADMIRE can be achieved than quality metrics based alone on physical measurements.

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This experimental study aims at filling the gap in the literature concerning the combined effects of hydroxyapatite (HA) concentration and elementary unit cell geometry on the biomechanical performances of additively manufactured polycaprolactone/hydroxyapatite (PCL/HA) scaffolds for tissue engineering applications. Scaffolds produced by laser powder bed fusion (LPBF) with diamond (DO) and rhombic dodecahedron (RD) elementary unit cells and HA concentrations of 5, 30 and 50 wt.% were subjected to structural, mechanical and biological characterization to investigate the biomechanical and degradative behavior from the perspective of bone tissue regeneration. Haralick's features describing surface pattern, correlation between micro- and macro-structural properties and human mesenchymal stem cell (hMSC) viability and proliferation have been considered. Experimental results showed that HA has negative influence on scaffold compaction under compression, while on the contrary it has a positive effect on hMSC adhesion. The unit cell geometry influences the mechanical response in the plastic regime and also has an effect on the cell proliferation. Finally, both HA concentration and elementary unit cell geometry affect the scaffold elastic deformation behavior as well as the amount of micro-porosity which, in turn, influences the scaffold degradation rate.

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Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a classical morphometric approach through a gray-level co-occurrence matrix and histogram analysis, as well as an approach based on deep learning semantic segmentation. From whole-slide images, pathologists selected 216 N and 201 MN BCC images. The two groups were then manually segmented and compared based on four morphological areas: center of the BCC islands (tumor, T), peripheral palisading of the BCC islands (touching tumor, TT), peritumoral cleft (PC) and surrounding stroma (S). We found that the TT pattern varied the least, while the PC pattern varied the most between the two subtypes. The combination of two distinct analysis approaches yielded fresh insights into the characterization of BCC, and thus, we were able to describe two different morphological patterns for the T component of the two subtypes.

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The accuracy of smear test image classification is a fundamental aspect in differentiating the type of leukaemia and determining the right treatment to improve the patient's chances of survival and recovery. Image Classification has lately become a very effective tool in detecting and analysing the right type of leukaemia as each type of the disease looks differently when evaluated under microscope. This paper is evaluating and comparing the efficiency and performance of feature extraction techniques (colour descriptors and Haralick texture descriptors) and a CNN (Convolutional Neural Network) built and trained by using the TensorFlow packages for classifying leukaemia images. Extracting texture and colour features from a given set of leukaemia images through computation was successful in detecting the type of disease and the results analysed with Weka Classifiers were giving the highest accuracy of 93.58%. TensorFlow tested with Cross-Validation proves efficient in training and customising the system, but the accuracy was median 56% and was not greatly improved by addressing the class imbalance issue from the data set with SMOTE. Further studies will investigate increasing the number of images by using a segmentation and image manipulation/augmentation techniques and increasing the accuracy of CNN through the addition of the investigated traditional features.

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Polyetheretherketone (PEEK) titanium composite (PTC) is a novel interbody fusion device that combines a PEEK core with titanium alloy (Ti6Al4V) endplates. The present study aimed to investigate the in vitro biological reactivity of human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) to micro- and nanotopographies produced by an acid-etching process on the surface of 3D-printed PTC endplates. Optical profilometer and scanning electron microscopy were used to assess the surface roughness and identify the nano-features of etched or unetched PTC endplates, respectively. The viability, morphology and the expression of specific osteogenic markers were examined after 7 days of culture in the seeded cells. Haralick texture analysis was carried out on the unseeded endplates to correlate surface texture features to the biological data. The acid-etching process modified the surface roughness of the 3D-printed PTC endplates, creating micro- and nano-scale structures that significantly contributed to sustaining the viability of hBM-MSCs and triggering the expression of early osteogenic markers, such as alkaline phosphatase activity and bone-ECM protein production. Finally, the topography of 3D-printed PTC endplates influenced Haralick's features, which in turn correlated with the expression of two osteogenic markers, osteopontin and osteocalcin. Overall, these data demonstrate that the acid-etching process of PTC endplates created a favourable environment for osteogenic differentiation of hBM-MSCs and may potentially have clinical benefit.

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PURPOSE: Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer. MATERIALS AND METHODS: After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation. RESULTS: Pretreatment MRI examination showed significant value (p < 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment. CONCLUSIONS: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients.

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The application of He-Ne laser technologies for description of articular cartilage degeneration, one of the most common diseases worldwide, is an innovative usage of these technologies used primarily in material engineering. Plain radiography and magnetic resonance imaging are insufficient to allow the early assessment of the disease. As surface roughness of articular cartilage is an important indicator of articular cartilage degeneration progress, a safe and noncontact technique based on laser speckle image to estimate the surface roughness is provided. This speckle image from the articular cartilage surface, when illuminated by laser beam, gives very important information about the physical properties of the surface. An experimental setup using a low power He-Ne laser and a high-resolution digital camera was implemented to obtain speckle images of ten bovine articular cartilage specimens prepared for different average roughness values. Texture analysis method based on gray-level co-occurrence matrix (GLCM) analyzed on the captured speckle images is used to characterize the surface roughness of the specimens depending on the computation of Haralick's texture features. In conclusion, this promising method can accurately estimate the surface roughness of articular cartilage even for early signs of degeneration. The method is effective for estimation of average surface roughness values ranging from 0.09 µm to 2.51 µm with an accuracy of 0.03 µm.

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PURPOSE: Establishing a framework to evaluate performances of prospective motion correction (PMC) MRI considering motion variability between MRI scans. METHODS: A framework was developed to obtain quantitative comparisons between different motion correction setups, considering that varying intrinsic motion patterns between acquisitions can induce bias. Intrinsic motion was considered by replaying in a phantom experiment the recorded motion trajectories from subjects. T1-weighted MRI on five volunteers and two different marker fixations (mouth guard and nose bridge fixations) were used to test the framework. Two metrics were investigated to quantify the improvement of the image quality with PMC. RESULTS: Motion patterns vary between subjects as well as between repeated scans within a subject. This variability can be approximated by replaying the motion in a distinct phantom experiment and used as a covariate in models comparing motion corrections. We show that considering the intrinsic motion alters the statistical significance in comparing marker fixations. As an example, two marker fixations, a mouth guard and a nose bridge, were evaluated in terms of their effectiveness for PMC. A mouth guard achieved better PMC performance. CONCLUSION: Intrinsic motion patterns can bias comparisons between PMC configurations and must be considered for robust evaluations. A framework for evaluating intrinsic motion patterns in PMC is presented.

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