Comparative evaluation of medical thermal image enhancement techniques for breast cancer detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis

In vivo evaluation of oxidized multiwalled-carbon nanotubes-mediated hyperthermia treatment for breast cancer

Breast cancer is one of the most common types of cancer that contribute to high mortality worldwide. Hyperthermia (HT) was introduced as one of the alternative treatments to treat breast cancer but has major drawback of damaging normal adjacent cells. This study explores the integration effect of multiwalled-carbon nanotubes (MWCNTs) in combination with hyperthermia treatment for breast cancer therapy regimes. In this study, acid-functionalized MWCNTs (ox-MWCNTs) were prepared by acid washing methods using H2SO4/HNO3 (98%/68%) with the ratio of 3:1 (?/?) and characterized by colloidal dispersibility test, FTIR, TGA, XRD, FESEM and EDX analysis. EMT6 tumor-bearing mice were treated with ox-MWCNTs in combination with local HT at 43 °C. The tumor progression was monitored and the influence of immune response was evaluated. Results from this study demonstrated that mice from ox-MWCNTs in combination with local HT treatment group experienced complete tumor eradication, accompanied by a significant increase in median survival of the mice. Histological and immunohistochemical analysis of tumor tissues revealed that tumor treated with combined treatment underwent cell necrosis and there was a significant reduction of proliferating cells when compared to the untreated tumor. This observation is also accompanied with an increase in Hsp70 expression in tumor treated with HT. Flow cytometry analysis of the draining lymph nodes showed an increase in dendritic cells infiltration and maturation in mice treated with combined treatment. In addition, a significant increase of tumor-infiltrated CD8+ and CD4+ T cells along with macrophages and natural killer cells was observed in tumor treated with combined treatment. Altogether, results presented in this study suggested the potential of ox-MWCNTs-mediated HT as an anticancer therapeutic agent, hence might be beneficial in the future of breast cancer treatment

A review on machine learning approaches in COVID-19 pandemic prediction and forecasting

Novel COVID-19 Coronavirus disease, namely SARS-CoV-2, is a global pandemic and has spread to more than 200 countries. The sudden rise in the number of cases is causing a tremendous effect on healthcare services worldwide. To assist strategies in containing its spread, machine learning (ML) has been employed to effectively track the daily infected and mortality cases as well as to predict the peak growth among the states or/and country-wise. The evidence of ML in tackling previous epidemics has encouraged researchers to reciprocate with this outbreak. In this paper, recent studies that apply various ML models in predicting and forecasting COVID-19 trends have been reviewed. The development in ML has significantly supported health experts with improved prediction and forecasting. By developing prediction models, the world can prepare and mitigate the spread and impact against COVID-19

Assessment of inspiration and expiration time using infrared thermal imaging modality

Breathing is one of the important vital signs assessed by healthcare practitioner for patient monitoring and disease management. There are several methods used to evaluate breathing activities such as respiratory inductive plethysmography, impedance pneumography, bioacoustics method, spirometry and manual assessment. Most of these devices require external attachment on patient such as belt, electrodes and sensor which could be inconvenient if used over a long period of time. Infrared Thermal Imaging (ITI) is a contactless device that detects temperature changes which can be used to assess breathing since hot air particles are being released to surrounding through nose which create temperature variance during breathing. Majority of studies conducted on breathing function were focused on respiratory rate. Therefore, this study assessed the timing of inspiration (TI) and expiration (TE) in three different breathing patterns which are normal, prolonged expiration and rapid breathing by using Infrared Thermal Imaging (ITI). A total of thirty-three subjects were required to simulate various breathing patterns by using a video-guided method. The assessment of TI and TE was recorded using ITI and Respiratory Inductive Plethysmograph (RIP) simultaneously. Results obtained from the ITI images show consistent deflections on the plotted graph which reflect the transition point of inspiration and expiration. This transition point allowed us to measure the TI and TE between ITI and RIP. Our analysis shows that there are no significant differences of the reading obtained between ITI and RIP in TI and TE. Correlation analysis also shows that there was positive correlation between measurement obtained by ITI and RIP. These findings suggest that ITI technique could be used as an alternative method to assess breathing dynamics

Stress distributions and micromovement of fragment bone of pilon fracture treated with external fixator: a finite element analysis

Osteoporosis and osteoarthritis are common pathological problems of the human bone tissue. There are some cases of pilon fractures associated with these 2 pathological conditions. In terms of treatment, for a normal and healthy bone with pilon fracture, the use of the Delta external fixator is a favorable option because it can allow early mobilization for patients and provide stability for the healing process. However, the stability of the external fixator differs when there is low bone stiffness, which has not been previously investigated. Therefore, this study was conducted to determine the stability of the external fixator to treat pilon fracture associated with osteoporosis and osteoarthritis, particularly to differentiate the stress distribution and micromovement of fracture fragment. Three-dimensional finite element models of the ankle and foot bones were reconstructed based on the computed tomography datasets. The bones consisted of 5 metatarsal, 3 cuneiform, and 1 each of cuboid, navicular, calcaneus, talus, fibula, and tibia bones. They were assigned with linear isotropic behavior. The ankle joint consisted of ligament and cartilage, and they were assigned with the use of linear links and the Mooney-Rivlin model, respectively. During simulation of the gait cycle, 70 N and 350 N were applied axially to the tibia bone to represent the swing and stance phases, respectively. The metatarsal and calcaneus bones were fixed to prevent any movement of the rigid body. The study found that the greatest von Mises stress value was observed at the pin–bone interface for the osteoporosis (108 MPa) model, followed by the osteoarthritis (87 MPa) and normal (44 MPa) models, during the stance phase. For micromovement, the osteoporosis model had the largest value at 0.26 mm, followed by the osteoarthritis (0.09 mm) and normal (0.03 mm) models. In conclusion, the greatest magnitudes of stress and micromovement were observed for the osteoporosis bone and extra care should be taken to treat pilon fracture associated with this pathological condition

Effect of short-term ketogenic diet on end-tidal carbon dioxide

Background & aims: Previous studies have shown that end-tidal carbon dioxide (EtCO2) is lower with the presence of supraphysiological ketones as in the case of chronic ketogenic diet (KD) and diabetic ketoacidosis (DKA). This study aimed to determine changes in EtCO2 upon short term KD. Methods: Healthy subjects were screened not to have conditions that exerts abnormal EtCO2 nor contraindicated for KD. Subjects underwent seven days of KD while the EtCO2 and blood ketone (beta-hydroxybutyrate; ß-OHB) parameters were sampled at day zero (t0) and seven (t7) of ketosis respectively. Statistically, the t-test and Pearson's coefficient were conducted to determine the changes and correlation of both parameters. Results: 12 subjects completed the study. The mean score ± standard deviation (SD) for EtCO2 were 35.08 ± 3.53 and 35.67 ± 3.31 mm Hg for t0 and t7 respectively. The mean score ±SD for ß-OHB were 0.07 ± 0.08 and 0.87 ± 0.84 mmol/L for t0 and t7 respectively. There was no significant difference of EtCO2 between the period of study (p > 0.05) but the ß-OHB increased during t7 (p < 0.05). There was also no correlation between the parameters. Conclusions: These findings suggest that EtCO2 may not be utilized to determine short term nutritional ketosis

From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research

Knee osteoarthritis is a major diarthrodial joint disorder with profound global socioeconomic impact. Diagnostic imaging using magnetic resonance image can produce morphometric biomarkers to investigate the epidemiology of knee osteoarthritis in clinical trials, which is critical to attain early detection and develop effective regenerative treatment/therapy. With tremendous increase in image data size, manual segmentation as the standard practice becomes largely unsuitable. This review aims to provide an in-depth insight about a broad collection of classical and deep learning segmentation techniques used in knee osteoarthritis research. Specifically, this is the first review that covers both bone and cartilage segmentation models in recognition that knee osteoarthritis is a “whole joint” disease, as well as highlights on diagnostic values of deep learning in emerging knee osteoarthritis research. Besides, we have collected useful deep learning reviews to serve as source of reference to ease future development of deep learning models in this field. Lastly, we highlight on the diagnostic value of deep learning as key future computer-aided diagnosis applications to conclude this review

A feasibility study of ultrasound as a monitoring method for hyperthermia therapy

Hyperthermia therapy is one of the therapy method used for cancer treatment. It has shown to be an effective way of treating the cancerous tissue when compared to surgery, chemotherapy and radiation. However, hyperthermia needs a real time monitoring method in ensuring a consistent heat delivery and preventing any damages to the nearby tissue. Ultrasound is one of the modalities that have great potential for local hyperthermia monitoring, as it is nonionizing, convenient, and has relatively simple signal processing requirement compared to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). B-Mode ultrasound provides sufficient temperature sensitivity and yields good spatial resolution for thermal monitoring meanwhile A Mode ultrasound involves only one-dimensional (1D) signal processing which enables a quantitative measurement on different types of breast tissues to be conducted faster. Therefore, this study was conducted to investigate and to compare the most optimum ultrasound temperature dependent's parameters in normal and pathological breast tissue between A-Mode and B-Mode ultrasound which involve the measurement of the attenuation and backscatter coefficients for A-Mode and determination of pixels value and standard deviation for B-Mode. For this purpose, a series of experiment was conducted on 40 female Sprague Dawley rats in which 30 pathological rats were used as infected study while 10 of healthy rats were group as control purposes. The pathological and normal rats were dissected and exposed to hyperthermia at 40°C, 45°C, 50°C and 55°C. Meanwhile, at 37°C was used as normal body temperature before hyperthermia. A-Mode and B-Mode of 7.5Mhz and 6Mhz was used simultaneously before, during and after the hyperthermia exposure. Result obtained shows that, for A-Mode, in both normal and infected tissue, the temperature value of 45°C was chosen to be an optimum temperature dependent for attenuation calculation and temperature value of 40°C was selected for backscatter energy. In B-Mode analysis, based on pixel values calculation of segmented area, result shows in normal tissues where the temperature value of 40°C was chosen, the standard deviation of 11.779 was obtained. Meanwhile for infected tissue condition, at 50°C the standard deviation value shown to be 7.95 as compared to the others temperature. Therefore, it is shown that, a combination of both A-Mode and B-Mode ultrasound can be used as another potential approach since its attenuation and backscatter coefficient of A-Mode, the pixels value and standard deviation of B-Mode is very sensitive to the tissue structure in monitoring hyperthermia therapy with respect to the changes of temperature

Analysis of breathing patterns from thermal images using an automated segmentation method

Breathing is one of the important vital signs in diagnosing and monitoring for patients' treatment and disease. Few modalities have been used to evaluate breathing activity such as respiratory belt, thermistor and capacitive sensor. However, these requires external attachments such as electrode or sensor which might be inconvenience over long period of time. Hence, we proposed the use of thermography as a contactless monitoring device. In this study, inspiration time and expiration time of three different breathing patterns such as normal, prolonged and rapid breathing patterns were measured by using the thermography. Thermal images obtained from the subjects were processed and analysed by using an automated segmentation method which integrate the knowledge of edge-based and region-based segmentation methods into the algorithm developed. The algorithm developed in this study has shown that the tracker was able to segment the region of interest of the thermal images automatically and it provides a more accurate and stable results than manual calculation method. Thus, three different types of breathing patterns could be identified based on the inspiration time to expiration time ratio. Results shows that there was less than 5% of relative error which suggest the benefit of this algorithm

A Comprehensive Review of the Recent Developments in Wearable Sweat-Sensing Devices

Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte&rsquo;s response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance