Motion Artifact Reduction in Breast Dynamic Infrared Imaging

Dynamic infrared imaging is a promising technique in breast oncology. In this study a QWIP infrared camera is used to acquire a sequence of consecutive thermal images of the patient's breast for 10 s. Information on the local blood perfusion is obtained from the spectral analysis of the time series at each image pixel. Due to respiratory and motion artifacts, the direct comparison of the temperature values that a pixel assumes along the sequence becomes difficult. In fact, the small temperature changes due to blood perfusion, of the order of 10-50 mK, which constitute the signal of interest in the time domain, are superimposed onto large temperature fluctuations due to the subject's motion, which represent noise. To improve the time series signal-to-noise ratio, and, as a consequence, enhance the specificity and sensitivity of the dynamic infrared examination, it is important to realign the thermal images of the acquisition sequence thus reducing motion artifacts. In a previous study we demonstrated that a registration algorithm based on fiducial points is suitable to both clinical applications and research, when associated with a proper set of skin markers. In this paper, we quantitatively evaluate the performance of different marker sets by means of a model that allows for estimating the signal-to-noise ratio increment due to registration, and we conclude that a 12-marker set is a good compromise between motion artifact reduction and the time required to prepare the patien

Reduction of gait abnormalities in type 2 diabetic patients due to physical activity: a quantitative evaluation based on statistical gait analysis

The aim of this study is the objective assessment of gait abnormalities in diabetic patients and the quantification of the benefits of physical activity in improving the gait quality. Patients were equipped with foot-switches and knee goniometers and were asked to walk at their natural pace for 2.5 minutes. A statistical gait analysis was performed extracting from hundreds of strides the ‘atypical' cycles, i.e. the cycles which do not show the usual sequence of gait phases (heel contact, flat foot contact, push off, swing), the duration of the heel contact phase and the knee kinematics in the sagittal plane. A sample population of 27 non-neuropathic type 2 diabetic patients was examined before and after attending a light-intensity physical activity program that lasted four months. A fuzzy classifier was used to assign a score to the gait abnormalities of each patient in baseline conditions and after the program completion. More than 50% of the subjects reduced significantly their gait abnormalities and, on the average, the most frequent improvements were the reduction of atypical cycles and heel contact duration. Furthermore we found that, in basal conditions, the left side is more affected by gait abnormalities than the right one (P < 0.003

Circular components in center of pressure signals

Static posturography provides an objective assessment of postural control by characterizing the body sway during upright standing. The Center-of-Pressure (CoP) signal is recorded by a force platform and it is analyzed by means of many different models and techniques. Most of the parameters calculated according to these different approaches are affected by relevant intra- and inter-subject variability and/or do not have a clear physiological interpretation. Traditional approaches decompose the CoP signal into antero-posterior and medio-lateral time series, corresponding to ankle plantar/dorsiflexion and hip adduction/abduction, respectively. In this study we hypothesized that CoP signals show inherent rotational characteristics. To verify our hypothesis we applied the rotary spectra analysis to the 2-dimensional CoP signal to decompose it into clockwise and counter-clockwise rotational components. We demonstrated the presence of rotational components in the CoP signal of healthy subjects, providing a reference data set of the spectral characteristics of these component

Evaluation of time-series registration methods in dynamic area telethermometry for breast cancer detection

Automated motion reduction in 3D dynamic infrared imaging is on demand in many applications. Few methods for registering time-series dynamic infrared frames have been proposed. Almost all such methods are feature based algorithms requiring manual intervention. We apply different automated registration methods based on spatial displacement to 11 datasets of Breast Dynamic Infrared Imaging (DIRI) and evaluate the results in terms of both the image similarity and anatomical consistency of the transformation. The aim is to optimize the registration strategy for breast DIRI in order to improve the spectral analysis of temperature modulation; thus facilitating the acquisition procedure in a Dynamic Area Telethermometry framework. The results show that symmetric diffeomorphic demons registration outperforms both warped frames similarity and smoothness of deformation fields; hence proving effective for time-series dynamic infrared registratio

Segmentation and classification of gait cycles

Gait abnormalities can be studied by means of instrumented gait analysis. Foot-switches are useful to study the foot-floor contact and for timing the gait phases in many gait disorders, provided that a reliable foot-switch signal may be collected. Considering long walks allows reducing the intra-subject variability, but requires automatic and user-independent methods to analyze a large number of gait cycles. The aim of this work is to describe and validate an algorithm for the segmentation of the foot-switch signal and the classification of the gait cycles. The performance of the algorithm was assessed comparing its results against the manual segmentation and classification performed by a gait analysis expert on the same signal. The performance was found to be equal to 100% for healthy subjects and over 98% for pathological subjects. The algorithm allows determining the atypical cycles (cycles that do not match the standard sequence of gait phases) for many different kinds of pathological gait, since it is not based on pathology-specific template

Physiological Electromyographic Activations Patterns of Lower Limb Muscle in Children

Kinesiological electromyography (KEMG) is an essential part of gait analysis as it supports clinicians with the objective assessment of muscular function during walking. During the gait cycle muscles are active with definite actions aimed at controlling joints in order to accomplish requirements of gait such as stability, loading acceptance, and progression. The knowledge of the development of normal EMG activity is of relevance in interpreting gait analysis data. While there is a wide literature on normative kinematics and kinetics data in children, only a few studies reported reference KEMG dataset on a paediatric population and on the maturation of gait in children. The available literature reported that the surface KEMG in children has a significant amount of variability, which should be taken into consideration when performing clinical interpretations. The KEMG timing and duration in normal children can vary with age, body height, body weight, gait velocity and stride length. Moreover, there is evidence that within session EMG variability in children aged 6-8 years is twice than that of adults. Although children in this age range can be considered to have a mature walk, variability about the mean performance continues to develop for many years and stable locomotion may be achieved despite significant variability in the muscle recruitment patterns. Further studies using accurate techniques of signal detection and analysis are required to improve our knowledge on physiological and pathological patterns of muscular activation in children