Electron Microscopy Image Segmentation

David Nam, Judith Mantell, David Bull, Paul Verkade, Alin Achim

The following work presents a graphical user interface (GUI), for automatic segmentation of granule cores and membranes, in transmission electron microscopy images of beta cells. The system is freely available for academic research. Two test images are also included. The highlights of our approach are:

  • A fully automated algorithm for granule segmentation.
  • A novel shape regularizer to promote granule segmentation.
  • A dual region-based active contour for accurate core segmentation.
  • A novel convergence filter for granule membrane verification.
  • A precision of 91% and recall of 87% is observed against manual segmentations.

Further details can be found in–

D. Nam, J. Mantell, D. Bull, P. Verkade, and A. Achim, “A novel framework for segmentation of secretory granules in electron micrographs,” Med. Image Anal., vol.18, no. 2, pp. 411–424, 2014.



Granule Segmenter Download (Matlab)

Mitigating the effects of atmospheric turbulence on surveillance imagery

Various types of atmospheric distortion can influence the visual quality of video signals during acquisition. Typical distortions include fog or haze which reduce contrast, and atmospheric turbulence due to temperature variations or aerosols. An effect of temperature variation is observed as a change in the interference pattern of the light refraction, causing unclear, unsharp, waving images of the objects. This obviously makes the acquired imagery difficult to interpret.

This project introduced a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to provide accurate detail from objects behind the distorting layer, a simple and efficient frame selection method is proposed to pick informative ROIs from only good-quality frames. We solve the space-variant distortion problem using region-based fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). We also propose an object alignment method for pre-processing the ROI since this can exhibit significant offsets and distortions between frames. Simple haze removal is used as the final step. We refer to this algorithm as CLEAR (for code please contact me) (Complex waveLEt fusion for Atmospheric tuRbulence). [PDF] [VIDEOS]

Atmospheric distorted videos of static scene

Mirage (256×256 pixels, 50 frames). Left: distorted sequence. Right: restored image. Download PNG


Download other distorted sequences and references [here].

Atmospheric distorted videos of moving object

Left: Distorted video. Right: Restored video. Download PNG


  • Atmospheric turbulence mitigation using complex wavelet-based fusion. N. Anantrasirichai, Alin Achim, Nick Kingsbury, and David Bull. IEEE Transactions on Image Processing. [PDF] [Sequences] [Code: please contact me]
  • Mitigating the effects of atmospheric distortion using DT-CWT fusion. N. Anantrasirichai, Alin Achim, David Bull, and Nick Kingsbury. In Proceedings of the IEEE International Conference on Image Processing (ICIP 2012). [PDF] [BibTeX]
  • Mitigating the effects of atmospheric distortion on video imagery : A review. University of Bristol, 2011. [PDF]
  • Mitigating the effects of atmospheric distortion. University of Bristol, 2012. [PDF]

Undecimated 2D Dual Tree Complex Wavelet Transforms

Dr Paul Hill, Dr Alin Achim and Professor Dave Bull

This work introduces two undecimated forms of the 2D Dual Tree Complex Wavelet Transform (DT-CWT) which combine the benefits of the Undecimated Discrete Wavelet Transform (exact translational invariance, a one-to-one relationship between all co-located coefficients at all scales) and the DT-CWT (improved directional selectivity and complex subbands).

The Discrete Wavelet Transform (DWT) is a spatial frequency transform that has been used extensively for analysis, denoising and fusion within image processing applications. It has been recognised that although the DWT gives excellent combined spatial and frequency resolution, the DWT suffers from shift variance. Various adaptations to the DWT have been developed to produce a shift invariant form. Firstly, an exact shift invariance has been achieved using the Undecimated Discrete Wavelet Transform (UDWT). However, the UDWT variant suffers from a considerably overcomplete representation together with a lack of directional selectivity. More recently, the Dual Tree Complex Wavelet Transform (DT-CWT) has given a more compact representation whilst offering near shift invariance. The DT-CWT also offers improved directional selectivity (6 directional subbands per scale) and complex valued coefficients that are useful for magnitude / phase analysis within the transform domain. This paper introduces two undecimated forms of the DT-CWT which combine the benefits of the UDWT (exact translational invariance, a one-to-one relationship between all co-located coefficients at all scales) and the DT-CWT (improved directional selectivity and complex subbands).

This image illustrates the three different 2D Dual Tree Complex Wavelet Transforms



Matlab code download 

Implementations of three complex wavelet transforms can be downloaded below as mex matlab files. They have been compiled in 32bit and 64bit windows and 64bit linux formats. If you need an alternative format please mail me at paul.hill@bristol.ac.uk. Code updated 16/7/2014.

Please reference the following paper if you use this software

Hill, P. R., N. Anantrasirichai, A. Achim, M. E. Al-Mualla, and D. R. Bull. “Undecimated Dual-Tree Complex Wavelet Transforms.” Signal Processing: Image Communication 35 (2015): 61-70.

The paper is available here: http://www.sciencedirect.com/science/article/pii/S0923596515000715

A previous paper is here:

Hill, P.; Achim, A.; Bull, D., “The Undecimated Dual Tree Complex Wavelet Transform and its application to bivariate image denoising using a Cauchy model,” Image Processing (ICIP), 2012 19th IEEE International Conference on , vol., no., pp.1205,1208, Sept. 30 2012-Oct. 3 2012.


Matlab Code Usage

Forward Transform: NDxWav2DMEX
Backward Transform: NDixWav2DMEX
Useage:  w = NDxWav2DMEX(x, J, Faf, af, nondecimate);
     y = NDixWav2DMEX(w, J, Fsf, sf, nondecimate);
x,y - 2D arrays
J - number of decomposition 
Faf{i}: tree i first stage analysis filters 
af{i}:  tree i filters for remaining analysis stages
Fsf{i}: tree i first stage synthesis filters 
sf{i}:  tree i filters for remaining synthesis stages
Nondecimated: 0 (default) for original decimated version, 1 for completely decimated version, 2 for decimation of just first level.
w – wavelet coefficients
w{a}{b}{c}{d} - wavelet coefficients
        a = 1:J (scales)
        b = 1 (real part); b = 2 (imag part)
        c = 1,2; d = 1,2,3 (orientations)
w{J+1}{a}{b} - lowpass coefficients
        a = 1,2; b = 1,2 
Example of Usage: 
  % Original Decimated Version
  x = rand(256,256);
  J = 4;
  [Faf, Fsf] = AntonB;
  [af, sf] = dualfilt1;
  w = NDxWav2DMEX(x, J, Faf, af,0);
  y = NDixWav2DMEX(w, J, Fsf, sf,0);
  err = x - y;
  % Decimated Version 1 (no decimation)
  x = rand(256,256);
  J = 4;
  [Faf, Fsf] = NDAntonB2; %(Must use ND filters for both)
  [af, sf] = NDdualfilt1;
  w = NDxWav2DMEX(x, J, Faf, af, 1);
  y = NDixWav2DMEX(w, J, Fsf, sf, 1);
  err = x - y;
  %Decimated Version 2 (decimation on only first level)
  x = rand(256,256);
  J = 4;
  [Faf, Fsf] = AntonB; 
  [af, sf] = NDdualfilt1; %(Must use ND filters for just these)
  w = NDxWav2DMEX(x, J, Faf, af, 2);
  y = NDixWav2DMEX(w, J, Fsf, sf, 2);
  err = x - y;
% (s/J^2) must be bigger than 5 (where s is both height and width)
% Height and width must be divisible by 2^J for fully decimated version
% Height and width must be divisible by 2 for nondecimated version 2

Hardware-accelerated Video Fusion

This projects aim at producing a low-power demonstrator for real-time video fusion using a hybrid SoC device that combines a low-power Cortex A9 multi-core processor and a FPGA fabric. The methodology involves using a fusion algorithm developed at Bristol based on Complex dual-tree wavelet transforms.  These transforms work in forward and inverse mode together with configurable fusion rules to offer high quality fusion output.

The complex dual-tree wavelet transforms represents  around 70% of total complexity. The wavelet accelerator designed at Bristol removes this complexity and accelerates the whole application by a factor of x4.  It also has a significant positive impact in overall energy. There is a negligible increase in power due to the fact that the fabric works in parallel with the main processor. Notice that if the optimization criteria is not performance or energy but power then the processor and fabric could reduce its clock frequency and voltage and obtain a significant reduction in power for the same energy and performance levels.

This project has built a system extended with frame capturing capabilities using thermal and visible light cameras.  In this link you can see the system working in our labs : hardware accelerated video fusion

This project has been funded by the Technology Strategy Board under their energy-efficient computers program with Qioptiq Ltd as industrial collaborator.

This research will be presented and demonstrated at FPL 2015, London in September.

Video super-resolution

Motion compensated video super-resolution is a technique that uses the sub-pixel shifts between multiple low resolution images of the same scene to create higher resolution frames with improved quality. An important concept is that due to the sub-pixel displacements of picture elements in the low resolution frames, it is possible to obtain high frequency content beyond the Nyquist limit of the sampling equipment. Super-resolution algorithms exploit the fact that as objects move in front of the camera sensor, picture elements captured in the camera pixels might not be visible in the next frame if the movement of the element does not extend to the next pixel. Super-resolution algorithms track and position these additional picture elements in the high-resolution frame. The resulting video quality is significantly improved compared with techniques that only exploit the information in one low-resolution frame to create one high resolution frame.

Super-Resolution techniques can be applied to many areas, including intelligent personal identification, medical imaging, security, surveillance and can be of special interest in applications that demand low-power and low-cost sensors. The key idea is that increasing the pixel size improves the signal to noise ratio and reduces the cost and power of the sensor.  Larger pixels enable more light to be collected and in addition the blur introduced by diffraction is reduced. Diffraction is a bigger issue with smaller pixels, so again sensors with larger pixels will perform better, giving sharper images with higher contrast in the fine details, especially in low-light conditions.

Benefits include that increasing the pixel size means that fewer pixels can be located in the sensor and this reduces the sensor resolution.  The low-resolution sensor needs to process and transmit a lower amount of information which results in lower power and cost.  Super-resolution algorithms running in the receiver side can then be used to recover high-quality and high-resolution videos maintaining a constant frame rate.

Overall, super-resolution enables the system that captures and transmits the video data to be based on low-power and low-cost components while the receiver still obtains a high-quality video stream.

This project has been sponsored by the Centre for Defence Enterprise and DSTL under the Generic Enablers for Low-Size, Weight, Power and Cost (SWAPC) Intelligence, Surveillance, Target Acquisition and Reconnaissance (ISTAR) program.

Click to see some examples :

1:  before  car number plate in and after super-resolution car number plate SR

2:  before vehicles in and after super-resolution vehicles SR

and learn about the theory behind the algorithm:  Chen, J, Nunez-Yanez, JL & Achim, A 2014, ‘Bayesian video super-resolution with heavy-tailed prior models’. IEEE Transactions on Circuits and Systems for Video Technology, vol 24., pp. 905-914