Maheza Irna Mohamad Salim

Thermal distribution analysis of three-dimensional tumor-embedded breast models with different breast density compositions

Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes’ bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.

Multiobjectives bihistogram equalization for image contrast enhancement

The global histogram equalization HE has been the most frequently adopted image contrast enhancement technique. A brightness and detail-preserving HE method with good contrast enhancement effect has been a goal of much recent research in HE. Nevertheless, producing a well-balanced HE is deemed to be a daunting task. In this article, we propose a novel framework of HE with the aim of taking three desirable properties into account: brightness preservation, detail preservation, and contrast enhancement. We termed the proposed method as multipurpose beta optimized bi-HE MBOBHE. MBOBHE consists of performing the histogram optimization separately in both subhistograms after the segmentation of histogram using an optimized separating point based on the three performance criteria using a weighted-sum aggregated objective function AOF. Both quantitative and qualitative results indicate that MBOBHE outperforms other existing bi-HE methods, in terms of comprehensive performance of HE that is capable of providing a holistic view.

Advances in Medical Diagnostic Technology

Of course, from childhood to forever, we are always thought to love reading. It is not only reading the lesson book but also reading everything good is the choice of getting new inspirations. Religion, sciences, politics, social, literature, and fictions will enrich you for not only one aspect. Having more aspects to know and understand will lead you become someone more precious. Yea, becoming precious can be situated with the presentation of how your knowledge much.

Review on Segmentation of Computer-Aided Skeletal Maturity Assessment

Bone age assessment (BAA) is an examination of ossification development with the purpose of deducing the skeletal age of children to monitor their skeletal development and predict their future adult height. Conventionally, it is performed by comparing left-hand radiographs to standard atlas by visual inspection; this process is subjective and time-consuming; therefore, the automated inspection system to overcome the drawbacks is established. However, the automated BAA system invariably confronts with problem in segmentation, which is the most crucial procedure in the computer-aided BAA. Inappropriate segmentation methods will produce unwanted noises that will affect the subsequent processes of the system. The current manual or semi-automated segmentation frameworks have impeded the system from becoming truly automated, objective, and efficient. The objective of this thesis is to provide a solution to the mentioned unsolved technical problem in segmentation for automated BAA system. The task is accomplished by first applying the modified histogram equalized module, then undergoing the proposed automated anisotropic diffusion, following by a novel fuzzy quadruple division scheme to optimize the central segmentation algorithm, and finally, the process ends with an additional quality assurance scheme. The designed segmentation framework works without the need of resources such as training sets and skillful operator. The quantitative and qualitative analysis of the resultant images have both shown that the designed framework is capable of separating the soft tissue and background from the hand bone with relatively high accuracy despite omitting the above-mentioned resources.

In Vivo Thermography-Based Image for Early Detection of Breast Cancer Using Two-Tier Segmentation Algorithm and Artificial Neural Network

Breast cancer is the most common form of cancer among women globally. Detecting a tumor at its early stages is very crucial for a higher possibility of successful treatment. Cancerous cells have high metabolic rate which generate more heat compared to healthy tissue and will be transferred to the skin surface. Thermography technique has distinguished itself as an adjunctive imaging modality to the current gold standard mammography approach due to its capability in measuring the heat radiated from the skin surface for early detection of breast cancer. It provides an additional set of functional information, describing the physiological changes of the underlying thermal and vascular properties of the tissues. However, the thermography technique is shown to be highly dependent on the trained analyst for image interpretation and most of the analyses were conducted qualitatively. Therefore, the current ability of this technique is still limited especially for massive screening activity. This chapter presented a proposed technical framework for automatic segmentation and classification of abnormality on multiple in vivo thermography-based images. A new two-tier automatic segmentation algorithm was developed using a series of thermography screening conducted on both pathological and healthy Sprague-Dawley rats. Features extracted show that the mean values for temperature standard deviation and pixel intensity of the abnormal thermal images are distinctively higher when compared to normal thermal images. For classification, Artificial Neural Network system was developed and produced a validation accuracy performance of 92.5% for thermal image abnormality detection. In conclusion, this study has successfully demonstrated that for massive or routine screening activities, the proposed technical framework could provide a highly reliable clinical decision support to the clinicians in making a diagnosis based on the medical thermal images.

Tumor localization in breast thermography with various tissue compositions by using Artificial Neural Network

Identifying and treating the tumor at its early stages has become one of the major challenges faced in the area of breast imaging field since the number of women diagnosed with breast cancer has gradually increase over the years. Breast thermography has distinguished itself as a promising adjunctive imaging modality to the current breast imaging standard for early detection of breast cancer. It provides additional information of underlying physiological changes of the cancerous tissues. However, this particular technique has not yet been accepted for clinical use for it is shown to be highly dependent on a trained operator and also due to the unavailability of a large clinical database for reference and classification. Therefore, this study proposed the development of Artificial Neural Network for tumor localization using thermal data obtained from the previous works. It utilized multiple features extracted from a series of numerical simulations conducted on various tissue composition breast models and were fed into the optimized ANN system of 6-8-1 network architecture with a learning rate of 0.2, an iteration rate of 20000 and a momentum constant value of 0.3. Result obtained shows that this newly developed ANN has a high performance accuracy percentage of 96.33% and 92.89% to both testing and validation data respectively.

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 dependents 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.

Measurement of Ultrasound Attenuation and Protein Denaturation Behavior During Hyperthermia Monitoring

Recently, there is increasing interest in the use of local hyperthermia therapy such as RF ablation, high intensity focused ultrasound and magnetic nanoparticles for a variety of clinical applications. The desired therapeutic outcome in these therapies is achieved by raising the local temperature to surpass the tissue coagulation threshold, resulting in tissue necrosis. However, the inability to closely monitor temperature elevation from hyperthermia therapy in real time with high accuracy continues to limit its clinical applicability. During therapy, temperature monitoring is essential for controlling thermal dose and this issue has been an ongoing difficulty in hyperthermia treatment. Ultrasound is an attractive and promising imaging modality to guide and monitor hyperthermia treatment because it is non-ionizing, inexpensive, portable and capable of real time imaging. Many methods for ultrasound thermometry have been presented previously including the manipulation of pixel value, the exploitation of thermal strain and most popular, the quantification of frequency dependence attenuation, backscatter coefficient and speed of sound in tissue. However, previous studies do not quantify the changes in ultrasound properties with microstructural and chemical changes in tissue. Hence, it is very hard to gauge the sensitivity of the reported ultrasound parameter to the given thermal intensity during hyperthermia treatment. This study focused on the quantification of ultrasound attenuation changes to total protein during hyperthermia therapy. In this study, A-Mode ultrasound was used to monitor a set of normal and DMBA-induced virgin female mice breast tissue in hyperthermia treatment at a temperature of 37, 55 and 65 °C. The objective was to investigate the relationship between thermal exposure during hyperthermia with changes in ultrasound attenuation and protein denaturation level. The application of heat on tissue in this study induced changes in the tissue structure due to protein coagulation. The heating process altered the way ultrasound propagates through the tissue, as protein coagulation changes tissue density and compressibility. From the result, it can be seen that ultrasound attenuation was very sensitive to changes in tissue microstructure due to hyperthermia. On the other hand, protein content in pathological tissues denatured at a higher rate compared to protein in normal tissue. Further study is ongoing to assess the relationship between attenuation, protein denaturation and thermal intensity. Ultrasound attenuation is expected to have high sensitivity to protein denaturation, which will be a good indicator for tissue necrosis.

Ultrasonic Elastography and Breast Imaging

The elastography is based on the principles: (1) Tissue compression produces strain (displacement) within the tissue, and (2) this strain is lower in harder tissues than in softer tissues. Therefore, by measuring tissue strain due to compression, tissue stiffness can be estimated. Since malignant breast tissue is generally harder than normal surrounding tissue, tissue hardness observed in elastography becomes the more precise clinical information than manual palpation. The use of quantitative elastography achieves the improvement in breast cancer diagnostic accuracy.

Sequential Process of Emotional Information from Facial Expressions: Simple Event-Related Potential (ERP) for the Study of Brain Activities

Human is socially living creature that needs to communicate with others. In direct communication, there are two ways in conveying the information: through speaking words or verbally and through facial expression, body gesture or non-verbally. The non-verbal communication is taken almost 70 % of humans’ communication. Therefore, to understand how this non-verbal information is processed by the brain is quite important. In this chapter, we would like to elucidate the process of the brain in understanding the facial expression by analyzing the brain signals that correspond to emotional content of facial expression. As known, the emotion can be differentiated into the type and the level of emotion. For example, though we know that smiley face and joyful face belong to the same type of happiness, we know that the level of happiness is higher in the joyful face. Therefore, how does the brain process this kind of type and level of emotional information is the basic question that we would like to answer in this chapter. In this chapter, we explain the way we collect the data, the step-by-step process of reducing the noise in the brain signals, the way of inter-correlating the behavioral data and brain signals, how we used those data to find the location of activated brain area for processing the specific content of emotion, and finally, how we exactly find that the process of understanding the emotional information from facial expression is a sequential process; understanding the type of emotion, followed by the level of that specific emotion. This emotional process is different from that of the process of understanding the physical content of the face, like identity and gender.