The Detection of Filaments in
Solar Images
Dr. Rami Qahwaji
Department of Electronic Imaging and Media
Communications
School of Informatics, Bradford University
Richmond Road, Bradford BD7 1DP, UK
Tel. +44(0)1274 236078
Fax. +44(0)1274 23372
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Organisation of Talk

Objective

Hybrid System
•
•
Detection of central solar region
Image enhancement and filtering
Detection of filament regions

Evaluating the performance

Future research directions and related topics
•
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
A fast detection system that provides automatic
detection for the filaments in solar images that are
observed from ground-based instruments, is presented.

The
aim is to design a robust and efficient detection
system that can extract the positions of filaments
regardless of their shapes, positions or sizes.
The
automatic detection is carried out using hybrid
techniques that detects the central solar region, filter the
image and then determine the position and size of the
filament regions.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.

The input to the detection system is a raw uncleaned
solar image.

The detection process is divided to three major steps.
•
Firstly, the central region of the solar disk is located
within the image.
•
Secondly, the detected central region is enhanced and
filtered.
•
Finally, the regions containing filaments within the
central solar region are highlighted.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Stage 1: The detection of the central solar
region

The central region in the solar disk is detected
in order to avoid the limb darkening effect and
the detection of incomplete filaments that
extend to or around the solar limb.

The morphological Filling algorithm is the
corner stone for the detection.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
The Modified HMT Algorithm
Step 1. Converting the input image to a binary image:
The splitting technique converts the input image to the binary image X.
Step 2. Implementing the hit-filter:
X is correlated hexagonally with a hit filter that detects the horizontal and
vertical edges to produce image H.
Step 3. Implementing the miss-filter:
X is correlated with the negative of the hit filter to produce M. The detected
edges are represented by white pixels M.
Step 4. Final AND operation:
H is ANDed with M to produce the output image.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Determining the Density of Objects using the
Watershed Transform (WST)
The digital image is represented in the 3-D space by considering its
brightness as a height coming out from the page toward the observer.
Hence, each binary image consists of mountains and valleys. The
valleys are assumed to be covered by rising water. At the point of
intersection a WST edge is found.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Detecting the central solar region
Step 1. Apply HMT to an input image in order to obtain image Z.
Step 2. Apply WST to image Z to obtain image W.
Step 3. For every horizontal edge in W, if a WST pixel emerges
from it then its position is highlighted.
Step 4. Each WST line detected is processed as follows:
W is scanned to determine if the WST line is vertically continuous.
• The WST line does not suffer from large horizontal displacements.
• The WST line goes downward until it meets a horizontal edge.
•
If all true, then then the WST line is given a false colour.
Step 5. For every false – coloured WST line, the black pixels that
separate it from its surrounding edge pixels are converted to
white to produce image F.
Step 6. Output image is obtained by ANDing F with Input image.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Stage 2: Image Filtering and Enhancement

Solar image enhancement aims to increase the greyscale differences between the active regions and
filaments.

This is implemented by finding the mean of the
central region and finding the maximum and minimum
grey scale values. The values of the pixels are recalculated in order to maximise the differences with
the mean without changing the mean's value.

Intensity filtering using low thresholds is applied
afterwards. The filaments and filaments-like regions
are darker in colour which enables an intensity filter
with a low threshold to indicate their positions and to
eliminate background
and active regions.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Stage 3: The detection of Filament Regions

This stage aims to detect the exact locations of
filaments with accuracy and speed.

The mosaic technique is implemented to
enhance the quality of the filtered regions.

The actual detection of filaments is carried out
using a sliding window that searches through
the mosaic image
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
The Mosaic Images
•A
mosaic image is constructed by decreasing the
resolution of the input image by a certain factor (n).
•The mosaic principle is used to enhance the detection
performance.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Detecting the Filaments in the Mosaic Image

A sliding window is moved along the mosaic image.
Every time the window reaches the upper edge of a
mosaic filament, a search for the lower, left and right
boundaries for the filament region is carried out.
 Once the boundaries of the filament are determined, the
filament pixels are painted in a false colour to prevent
them from being detected again by another moving
window.
 At the end of this process, variable size windows are
defined and stored in multiple size arrays and
represented later in a data file that can be input to a
neural network or region growing code to be verified as
a filament region.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
The detection algorithm detects the filaments in a 1024
× 1024 image in about 0.8 seconds using P4-2.0 G Hz
PC with 512 M Byte RAM.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Evaluating the performance
Defining error metrics

In order to evaluate the performance of the filament
detector, the following two error rates are introduced:

The false acceptance rate (FAR): probability of a nonfilament object being detected as a filament.

The false rejection rate (FRR): the probability of a
filament not being detected because it is considered to
be a non-filament object.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.

The detection algorithm was applied to Meudon HAlpha solar images for the period from 1/ 7 / 2001 till
5/ 8/ 2001.

For every H-alpha image used, a corresponding
manually constructed synoptic map that contains the
locations of filaments exists.

These filament maps are obtained manually using the
subjective analysis of solar observers.

The FAR and FRR error parameters are established by
comparing the detected filaments, which are generated
using the current detection algorithm, with those
detected manually and recorded in the synoptic maps.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
The way Forward

In the future, a neural network verification stage will be
added to the current detection technique for verification
purposes.

The neural network could be also used to provide
understanding of the relation between the filament regions
that are detected over successive days.
 Elimination of sun spots using regional morphological
filters.

Detection of neutral lines in MDI images and comparing
these results with the automatic detection with filaments
and Active regions.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Enhancing the Solar image
slope=(float)(i-j)/(float)(pos_right[i]-pos_left[j]);
new_y=i-slope*(pos_right[i]-x);
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Detection of Neutral lines

The neutral lines in the MDI images are detected by
applying morphological edge detection to the
smoothed image.

Multi-level smoothing is designed.

The hexagonal sampling is applied to find the average
energy for every pixel in the image.

The average energy is then compared with a local
region threshold that is determined based on the
smoothing level.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.

The smoothing is carried out using the following
steps:
1. the energy surrounding every pixel is calculated in a
hexagonal manner.
2. The output energy is compared with an energy
threshold. According to the comparison outcome,
three regions will be obtained:
•
•
•
The blue regions are obtained when the energy exceeds
the detection threshold.
The red regions are obtained when the energy is less than
the detection threshold.
White regions are obtained when the energy lies within
the detection threshold region.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.

Additional step is added to handle the white regions.
 The energy surrounding every white pixel is again
calculated using a larger hexagonal filter and again the
calculated energy is compared. If no decision can be
made then a larger window is used and the process is
repeated.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
The input image, Taken on the 23rd July, 2002
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Different smoothing levels
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Current work

Comparing the neutral lines with active regions and
with filaments
 The statistical distribution of the neutral lines within
the automatically detected active regions and
filaments is studied. The aim is to verify whether a
relation exist or can be established.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.
Thanks for listening
Solar Image Recognition Workshop,
Brussels, 23 & 24 Oct.