Observations on adaptive vector filters for noise reduction in color images

In a series of papers, Plataniotis et al. proposed a number of filters for noise reduction in color images where the noise type is unknown. In this letter, those filters with a unified notation are summarized, and it is shown that they are essentially variants of the same filtering procedure. It is also shown that the class of adaptive vector filters can be considered as interpolants between the arithmetic mean filter and the vector median filter. Results are presented of numerical computations with the filters on test images corrupted with noise. It is found that the adaptive vector filters perform well with general applicability

Designing personalised cancer treatments

The concept of personalised medicine for cancer is not new. It arguably began with the attempts by Salmon and Hamburger to produce a viable cellular chemosensitivity assay in the 1970s, and continues to this day. While clonogenic assays soon fell out of favour due to their high failure rate, other cellular assays fared better and although they have not entered widespread clinical practice, they have proved to be very useful research tools. For instance, the ATP-based chemosensitivity assay was developed in the early 1990s and is highly standardised. It has proved useful for evaluating new drugs and combinations, and in recent years has been used to understand the molecular basis of drug resistance and sensitivity to anti-cancer drugs. Recent developments allow unparalleled genotyping and phenotyping of tumours, providing a plethora of targets for the development of new cancer treatments. However, validation of such targets and new agents to permit translation to the clinic remains difficult. There has been one major disappointment in that cell lines, though useful, do not often reflect the behaviour of their parent cancers with sufficient fidelity to be useful. Low passage cell lines — either in culture or xenografts are being used to overcome some of these issues, but have several problems of their own. Primary cell culture remains useful, but large tumours are likely to receive neo-adjuvant treatment before removal and that limits the tumour types that can be studied. The development of new treatments remains difficult and prediction of the clinical efficacy of new treatments from pre-clinical data is as hard as ever. One lesson has certainly been that one cannot buck the biology — and that understanding the genome alone is not sufficient to guarantee success. Nowhere has this been more evident than in the development of EGFR inhibitors. Despite overexpression of EGFR by many tumour types, only those with activating EGFR mutations and an inability to circumvent EGFR blockade have proved susceptible to treatment. The challenge is how to use advanced molecular understanding with limited cellular assay information to improve both drug development and the design of companion diagnostics to guide their use. This has the capacity to remove much of the guesswork from the process and should improve success rates

Designing personalised cancer treatments

The concept of personalised medicine for cancer is not new. It arguably began with the attempts by Salmon and Hamburger to produce a viable cellular chemosensitivity assay in the 1970s, and continues to this day. While clonogenic assays soon fell out of favour due to their high failure rate, other cellular assays fared better and although they have not entered widespread clinical practice, they have proved to be very useful research tools. For instance, the ATP-based chemosensitivity assay was developed in the early 1990s and is highly standardised. It has proved useful for evaluating new drugs and combinations, and in recent years has been used to understand the molecular basis of drug resistance and sensitivity to anti-cancer drugs. Recent developments allow unparalleled genotyping and phenotyping of tumours, providing a plethora of targets for the development of new cancer treatments. However, validation of such targets and new agents to permit translation to the clinic remains difficult. There has been one major disappointment in that cell lines, though useful, do not often reflect the behaviour of their parent cancers with sufficient fidelity to be useful. Low passage cell lines — either in culture or xenografts are being used to overcome some of these issues, but have several problems of their own. Primary cell culture remains useful, but large tumours are likely to receive neo-adjuvant treatment before removal and that limits the tumour types that can be studied. The development of new treatments remains difficult and prediction of the clinical efficacy of new treatments from pre-clinical data is as hard as ever. One lesson has certainly been that one cannot buck the biology — and that understanding the genome alone is not sufficient to guarantee success. Nowhere has this been more evident than in the development of EGFR inhibitors. Despite overexpression of EGFR by many tumour types, only those with activating EGFR mutations and an inability to circumvent EGFR blockade have proved susceptible to treatment. The challenge is how to use advanced molecular understanding with limited cellular assay information to improve both drug development and the design of companion diagnostics to guide their use. This has the capacity to remove much of the guesswork from the process and should improve success rates

Colour normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in colour retinal images

Images of the human retina vary considerably in their appearance depending on the skin pigmentation (amount of melanin) of the subject. Some form of normalisation of colour in retinal images is required for automated analysis of images if good sensitivity and specificity at detecting lesions is to be achieved in populations involving diverse races. Here we describe an approach to colour normalisation by shade-correction intra-image and histogram normalisation inter-image. The colour normalisation is assessed by its effect on the automated detection of microaneurysms in retinal images. It is shown that the Na¨ıve Bayes classifier used in microaneurysm detection benefits from the use of features measured over colour normalised images

Volume measurement using 3D Range Imaging

The use of 3D Range Imaging has widespread applications. One of its applications provides us the information about the volumes of different objects. In this paper, 3D range imaging has been utilised to find out the volumes of different objects using two algorithms that are based on a straightforward means to calculate volume. The algorithms implemented succesfully calculate volume on objects provided that the objects have uniform colour. Objects that have multi-coloured and glossy surfaces provided particular difficulties in determining volume

A high-resolution full-field range imaging system

There exist a number of applications where the range to all objects in a field of view needs to be obtained. Specific examples include obstacle avoidance for autonomous mobile robots, process automation in assembly factories, surface profiling for shape analysis, and surveying. Ranging systems can be typically characterized as being either laser scanning systems where a laser point is sequentially scanned over a scene or a full-field acquisition where the range to every point in the image is simultaneously obtained. The former offers advantages in terms of range resolution, while the latter tend to be faster and involve no moving parts. We present a system for determining the range to any object within a camera's field of view, at the speed of a full-field system and the range resolution of some point laser scans. Initial results obtained have a centimeter range resolution for a 10 second acquisition time. Modifications to the existing system are discussed that should provide faster results with submillimeter resolution