Jansen‐MIDAS: A multi‐level photomicrograph segmentation software based on isotropic undecimated wavelets

Image segmentation, the process of separating the elements within a picture, is frequently used for obtaining information from photomicrographs. Segmentation methods should be used with reservations, since incorrect results can mislead when interpreting regions of interest (ROI). This decreases the success rate of extra procedures. Multi‐Level Starlet Segmentation (MLSS) and Multi‐Level Starlet Optimal Segmentation (MLSOS) were developed to be an alternative for general segmentation tools. These methods gave rise to Jansen‐MIDAS, an open‐source software. A scientist can use it to obtain several segmentations of hers/his photomicrographs. It is a reliable alternative to process different types of photomicrographs: previous versions of Jansen‐MIDAS were used to segment ROI in photomicrographs of two different materials, with an accuracy superior to 89%.

[1]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[2]  Alexandre Fioravante de Siqueira,et al.  Segmentation of scanning electron microscopy images from natural rubber samples with gold nanoparticles using starlet wavelets , 2014, Microscopy research and technique.

[3]  Jon H. Kaas,et al.  How to count cells: the advantages and disadvantages of the isotropic fractionator compared with stereology , 2015, Cell and Tissue Research.

[4]  Alexandre Fioravante de Siqueira,et al.  Estimating the concentration of gold nanoparticles incorporated on natural rubber membranes using multi-level starlet optimal segmentation , 2014, Journal of Nanoparticle Research.

[5]  O. Scherzer.  Handbook of mathematical methods in imaging , 2011 .

[6]  Fan Yang,et al.  A Sunblock Based On Bioadhesive Nanoparticles , 2015, Nature materials.

[7]  Alexandre Fioravante de Siqueira,et al.  An automatic method for segmentation of fission tracks in epidote crystal photomicrographs , 2014, Comput. Geosci..

[8]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[9]  M. Basei,et al.  Ediacaran 2,500-km-long synchronous deep continental subduction in the West Gondwana Orogen , 2014, Nature Communications.

[10]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[11]  Fionn Murtagh,et al.  Starlet Transform in Astronomical Data Processing , 2015, Handbook of Mathematical Methods in Imaging.

[12]  Pascal Fua,et al.  Dendritic tree extraction from noisy maximum intensity projection images in C. elegans , 2014, Biomedical engineering online.

[13]  Pierre Baldi,et al.  Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..

[14]  Gilson Luiz Volpato,et al.  Death-associated odors induce stress in zebrafish , 2014, Hormones and Behavior.

[15]  D. Fernig,et al.  Determination of size and concentration of gold nanoparticles from UV-vis spectra. , 2007, Analytical chemistry.

[16]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[17]  T. Ehlers,et al.  Advantages and challenges of automated apatite fission track counting , 2012 .

[18]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[19]  P. Bleuet,et al.  Self-adapting denoising, alignment and reconstruction in electron tomography in materials science. , 2016, Ultramicroscopy.

[20]  Lan Lin,et al.  A template‐based automatic skull‐stripping approach for mouse brain MR microscopy , 2013, Microscopy research and technique.

[21]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[22]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[23]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[24]  Mohamed-Jalal Fadili,et al.  Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity, by Jean-Luc Starck, Fionn Murtagh, and Jalal M. Fadili , 2010, J. Electronic Imaging.

[25]  Miguel Angel Rodriguez-Perez,et al.  Characterization of the cellular structure based on user-interactive image analysis procedures , 2013 .

[26]  Richard Kronland-Martinet,et al.  A real-time algorithm for signal analysis with the help of the wavelet transform , 1989 .

[27]  Hyunjin Park,et al.  Corrigendum: Sound Packing DNA: packing open circular DNA with low-intensity ultrasound , 2015, Scientific Reports.

[28]  K. Wang,et al.  A counting method for density packed cells based on sliding band filter image enhancement , 2013, Journal of microscopy.

[29]  Jean-Christophe Olivo-Marin,et al.  Tracking fluroescent spots in biological video microscopy , 2003, SPIE BiOS.

[30]  Jeroen Lammertyn,et al.  Semi-automatic size measurement of primary particles in aggregated nanomaterials by transmission electron microscopy , 2014 .

[31]  Jean-Luc Starck,et al.  Astronomical image and data analysis , 2002 .

[32]  Mohamed-Jalal Fadili,et al.  The Undecimated Wavelet Decomposition and its Reconstruction , 2007, IEEE Transactions on Image Processing.

[33]  He Jia,et al.  Investigation of reactions between trace gases and functional CuO nanospheres and octahedrons using NEXAFS-TXM imaging , 2015, Scientific Reports.

[34]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[35]  D. Belton,et al.  Coincidence mapping - a key strategy for the automatic counting of fission tracks in natural minerals , 2009 .